* Revenue from tickets/fines/etc. shouldn't go to into police pockets or it just incentives rent seeking behavior by people with law enforcement powers
* Settlements should come out of their budget not city insurance.
That and we might get some kind of judicial ruling that current incarnations of facial recognition software are racially based.
It would be a great result if a court declared that the use of racially biased facial recognition software is a violation of the 14th amendment violation, and enjoined PDs from using such software unless it can be demonstrated to be free of racial bias.
Will he? I thought it was pretty hard to win cases against the police. What does the evidence about the practicality of pursuing police for torts say here, and for that matter what kind of evidence should we be looking for?
One can file a civil case for false arrest. However, thanks to the qualified immunity cops enjoy coupled with the newness of the facial recognition technology, it will be very hard to prove that they obviously violated clearly established law. Maybe the cops will even point their fingers at the software.
Maybe he can go after the makers of the facial recognition software, but they can probably point their finger at the cops for using it wrong.
So, in any case, the guy will be left with a big legal bill.
> How many people actually stick to the page and listen to that?
I just did. 3 minutes wasn't that bad and I wasn't somewhere where it would be a problem.
> Why do sites do this?
NPR is a radio network. I have seen that often they do transcribe their clips. I am not sure what the process they have for that looks like, but it seems this particular clip didn't get transcribed.
Edit: looks like they do have a transcription mentioned elsewhere in the thread. So seems like some kind of UI fail.
NPR does transcribe (many, most?) its audio stories, but usually there's a delay of a day or so – the published timestamp for this story is 5:06AM (ET) today.
edit: looks like there's a text version of the article. I'm assuming this is a CMS issue: there's an audio story and a "print story", but the former hadn't been linked to the latter: https://news.ycombinator.com/item?id=23628790
More than zero. It's called closed captioning, isn't it? I've quite often seen closed-captioning that put brief written descriptions of non-verbal depictions in bracket, and it's not entirely common either
Sorry, I thought that since we were originally talking about transcriptions of radio news broadcasts and accessibility for the hard of hearing that closed-captioning would be appropriate and relevant. But your point is well met.
NPR is fantastic when it comes to accessibility by providing transcripts. I linked the page thinking the transcript will come later as they usually do. But turns out it was a wrong link. See elsewhere for the correct link.
Most people are going to hear the story on the radio or in a podcast app / RSS feed. It’s useful to have the story indexed on a shareable web link where it can be played on different platforms without any setup. If I wanted to share a podcast episode with friends in a group chat, a link like this would be a good way to do it. Since this is more of a long-form text discussion forum I’d probably look for a text format before posting here.
I don't think using the facial recognition is necessarily wrong to help identify probable suspects, but arresting someone based on a facial match algorithm is definitely going too far.
Of course really I blame the AI/ML hucksters for part of this mess who have sold us the idea of machines replacing rather than augmenting human decision making.
I think it is very wrong. Faces are anything but unique. Having a particular face should not result in you being a suspect. Only once actual policing results in you becoming a suspect then this might be a low quality extra signal.
> Having a particular face should not result in you being a suspect. Only once actual policing results in you becoming a suspect then this might be a low quality extra signal.
Having a picture or just a description of the face is one of the most important pieces of information the police has in order to do actual policing. You can be arrested for just broadly matching the description if you happen to be in the vicinity.
Had the guy been convicted of anything just based on that evidence, this would be a scandal. As it is, a suspect is just a suspect and this kind of thing happens all the time, because humans are just as fallible. It's just not news when there's no AI involved.
A face of which there is only a description is not going to work if there aren't any special identifying marks unless you get an artist involved or one of those identikit sets to reconstruct the face. An AI is just going to spit out some generic representation of what it was trained on rather than the specifics of the face of an actual suspect.
Faces generated by AI means should not count as 'probable cause' to go and arrest people. They should count as fantasy.
There was further work involved, there was a witness who identified the man on a photo lineup, and so on. The AI did not identify anyone, it gave a "best effort" match. All the actual mistakes were made by humans.
In a world where some police forces don't use polygraph lie detectors because they are deemed too inaccurate, it baffles me that people would make an arrest based on a facial recognition hit from poor quality data.
This seems similar to self-driving cars where people hold the computer to much higher standards than humans. I don't have solid proof, but I suspect that using facial recognition with a reasonable confidence threshold and reasonable source images is more accurate than eyewitness ID. If for no other reason than the threshold for a positive eyewitness ID is laughably bad.
The current best practice is to have a witness pick out the suspect from 6 photos. It should be immediately obvious that right off the bat there's a 17% chance of the witness randomly picking the "right" person. It's a terrible way to do things and it's no surprise that people are wrongly convicted again and again on eyewitness testimony.
You need a much higher standard standard of accuracy for facial recognition because it is applied indiscriminately to a large population. If it has 99.9% accuracy and you apply it to a population of 10,000 people, you will get on average 10 false positives.
Yeah, facial recognition can be useful in law enforcement, as long as it's used responsibly. There was a man who shot people at a newspaper where I lived, and when apprehended, he refused to identify himself, and apparently their fingerprint machine wasn't working, so they used facial recognition to identify him.
Agreed, I'm not saying we can currently assume they are responsible, but in some hypothetical future where reforms have been made and they can be trusted, I think it would be fine to use. I don't think we should use current bad actors to decide that a technology is completely off limits in the future.
From the wiki article and the linked news articles, the police picked him up at the scene of the crime. He also had smoke grenades (used in the attack) when they found him.
> Authorities said he was not carrying identification at the time of his arrest and was not cooperating. … an issue with the fingerprint machine ultimately made it difficult to identify the suspect, … A source said officials used facial recognition technology to confirm his identity.
> Police, who arrived at the scene within a minute of the reported gunfire, apprehended a gunman found hiding under a desk in the newsroom, according to the top official in Anne Arundel County, where the attack occurred.
This doesn't really seem like an awesome use of facial recognition to me. He was already in custody after getting picked up at the crime scene. I doubt he would have been released if facial recognition didn't exist.
I don't think there is such a thing as responsible use of facial recognition technology by law enforcement.
The technology is certainly not robust enough to be trusted to work correctly at that level yet. Even if it was improved I think there is a huge moral issue with the police having the power to use it indiscriminately on the street.
This story is really alarming because as described, the police ran a face recognition tool based on a frame of grainy security footage and got a positive hit. Does this tool give any indication of a confidence value? Does it return a list (sorted by confidence) of possible suspects, or any other kind of feedback that would indicate even to a layperson how much uncertainty there is?
The issue of face recognition algorithms performing worse on dark faces is a major problem. But the other side of it is: would police be more hesitant to act on such fuzzy evidence if the top match appeared to be a middle-class Caucasian (i.e. someone who is more likely to take legal recourse)?
Intresting and related, a team made a neat "face depixelizer" that takes a pixelated image and uses machine learning to generate a face that should match the pixelated image.
What's hilarious is that it makes faces that look nothing like the original high-resolution images.
One of the underlying models, PULSE, was trained on CelebAHQ, which is likely what the results are mostly white-looking. StyleGAN, which was trained on the much more diverse (but sparse) FFHQ dataset does come up with a much more diverse set of faces[1]...but PULSE couldn't get them to converge very closely on the pixelated subjects...so they went with CelebA [2].
What's sad is that a tech entrepreneur will definitely add that feature and sell it to law enforcement agencies that believe in CSI magic: https://www.youtube.com/watch?v=Vxq9yj2pVWk
A better strategy might be to pixelate a photo of each member of the jury, than de-pixelate it through the same service, and distribute the before and after. Maybe include the judge and prosecutor.
Interesting... Neat... Hilarious... In light of the submission and the comment you're responding to, these are not the words I would choose.
I think there's genuine cause for concern here, especially if technologies like these are candidates for inclusion in any real law enforcement decision-making.
Ironically, if the police had used and followed the face depixelizer then we may not have had the false arrest of a black man - not because of accuracy but because it doesn't produce many black faces
People are not good at understanding uncertainty and its implications, even if you put it front and center. I used to work in renewable energy consulting and I was shocked by how aggressively uncertainty estimates are ignored by those whose goals they threaten.
In this case, it's incumbent on the software vendors to ensure that less-than-certain results aren't even shown to the user. American police can't generally be trusted to understand nuance and/or do the right thing.
I blame TV shows like CSI and all the other crap out there that make pixelated images look like something you could "Zoom" into or something the computer can still understand even if the eye does not. Because of this, non tech people do not really understand that pixelated images have LOST information. Add that to the racial situation in the U.S. and the the inaccuracy of the tool for black people. Wow, this can lead to some really troublesome results
> Does this tool give any indication of a confidence value?
Yes.
> Does it return a list (sorted by confidence) of possible suspects,
Yes.
> ... or any other kind of feedback that would indicate even to a layperson how much uncertainty there is?
Yes it does. It also states in large print heading “THIS DOCUMENT IS NOT A POSITIVE IDENTIFICATION IT IS AN INVESTIGATIVE LEAD AND IS NOT PROBABLE CAUSE TO ARREST”.
You can see a picture of this in the ACLU article.
The police bungled this badly by setting up a fake photo lineup with the loss prevention clerk who submitted the report (who had only ever seen the same footage they had).
However, tools that are rife for misuse do not get a pass because they include a bold disclaimer. If the tool/process can not prevent misuse, the tool/process is broken and possibly dangerous.
That said, we have little data on how often the tool results in catching dangerous criminals versus how often it misidentifies innocent people. We have little data on if those innocent people tend to skew toward a particular demographic.
But I have a fair suspicion that dragnet techniques like this unfortunately can be both effective and also problematic.
I think the software would be potentially less problematic if the victim/witness were given access, and (ostensibly) could see the pool of matches and how much/little the top likely match differed from the less confident matches.
> The police bungled this badly by setting up a fake photo lineup...*
FWIW, this process is similar to traditional police lineups. The witness is shown 4-6 people – one who is the actual suspect, and several that vaguely match a description of the suspect. When I was asked to identify a suspect in my robbery, the lineup included an assistant attorney who would later end up prosecuting the case. The police had to go out and find tall slight-skinned men to round out the lineup.
> ... with the loss prevention clerk who submitted the report (who had only ever seen the same footage they had).
Yeah, I would hope that this is not standard process. The lineup process is already imperfect and flawed as it is even with a witness who at least saw the crime first-hand.
Essentially, an employee of the facial recognition provider forwarded an "investigative lead" for the match they generated (which does have a score associated with it on the provider's side, but it's not clear if the score is clearly communicated to detectives as well), and the detectives then put the photo of this man into a "6 pack" photo line-up, from which a store employee then identified that man as being the suspect.
Everyone involved will probably point fingers at each other, because the provider for example put large heading on their communication that, "this is not probable cause for an arrest, this is only an investigative lead, etc.", while the detectives will say well we got a hit from a line-up, blame the witness, and the witness would probably say well the detectives showed me a line-up and he seemed like the right guy (or maybe as is often the case with line-ups, the detectives can exert a huge amount of bias/influence over witnesses).
EDIT: Just to be clear, none of this is to say that the process worked well or that I condone this. I think the data, the technology, the processes, and the level of understanding on the side of the police are all insufficient, and I do not support how this played out, but I think it is easy enough to provide at least some pseudo-justification at each step along the way.
That's interesting. In many ways, it's similar to the "traditional" process I went through when reporting a robbery to the NYPD 5+ years ago: they had software where they could search for mugshots of all previously convicted felons living in a x-mile radius of the crime scene, filtered by the physical characteristics I described. Whether the actual suspect's face was found by the software, it was ultimately too slow and clunky to paginate through hundreds of results.
Presumably, the facial recognition software would provide an additional filter/sort. But at least in my situation, I could actually see how big the total pool of potential matches and thus have a sense of uncertainty about false positives, even if I were completely ignorant about the impact of false negatives (i.e. what if my suspect didn't live within x-miles of the scene, or wasn't a known/convicted felon?)
So the caution re: face recognition software is how it may non-transparently add confidence to this already very imperfect filtering process.
(in my case, the suspect was eventually found because he had committed a number of robberies, including being clearly caught on camera, and in an area/pattern that was easy to narrow down where he operated)
I'm becoming increasingly frustrated with the difficulty in accessing primary source material. Why don't any of these outlets post the surveillance video and let us decide for ourselves how much of a resemblance there is.
I don't know what passage you're describing, but this one is implied to be part of a narrative that is told from the perspective of Mr. Williams, i.e. he's the one who remembers "The photo was blurry, but it was clearly not Mr. Williams"
> The detective turned over the first piece of paper. It was a still image from a surveillance video, showing a heavyset man, dressed in black and wearing a red St. Louis Cardinals cap, standing in front of a watch display. Five timepieces, worth $3,800, were shoplifted.
> “Is this you?” asked the detective.
> The second piece of paper was a close-up. The photo was blurry, but it was clearly not Mr. Williams. He picked up the image and held it next to his face.
All the preceding grafs are told in the context of "this what Mr. Williams said happened", most explicitly this one:
> “When’s the last time you went to a Shinola store?” one of the detectives asked, in Mr. Williams’s recollection.
According to the ACLU complaint, the DPD and prosecutor have refused FOIA requests regarding the case:
Maybe it's just me, but "we just took his word for it" doesn't strike me as particularly good journalism if that's what happened. If they really wrote these articles without that level of basic corroboration then that's pretty bad.
It's a common technique in journalism to describe and attribute someone's recollection of events in a series of narrative paragraphs. It does not imply "we just took his word for it", though it does imply that the reporter finds his account to be credible enough to be given some prominent space.
This arrest happened 6 months ago. Who else besides the suspect and the police do you believe reporters should ask for "basic corroboration" of events that took place inside a police station? Or do you think this story shouldn't be reported on at all until the police agree to give additional info?
I fell out of love with NPR and Ira Glass a long time ago.
NPR is unfortunately an actor in the current culture war.
The subject of the story is a POC, therefore by definition innocent victim of "systemic racism", "white supremacy", "implicit bias" or whatever fighting word the left comes up.
Check any past story where the subject of the story is a POC, those are never in any way responsible for their own misfortunes.
If in the current story time will show that the investigative lead was indeed correct to point Mr Williams you won't ever see a retraction or update on NPR.
>They are in the business of exposing you to as many paid ads as possible.
NPR is a non-profit that is mostly funded by donations. They only have minimal paid ads on their website to pay for running costs - they could easily optimize the news pages to increase ad revenue but they don't because it would get in the way of their goals.
> and the detectives then put the photo of this man into a "6 pack" photo line-up, from which a store employee then identified that man as being the suspect.
This is absurdly dangerously. The AI will find people who look like the suspect, that’s how the technology works. A lineup as evidence will almost guarantee a bad outcome, because of course the man looks like the suspect!
Excellent point. In fact, the entire process of showing the witness the photos should be recorded, and double blind. I.e the officer showing the person should not know anything about the lineup.
> Essentially, an employee of the facial recognition provider forwarded an "investigative lead" for the match they generated (which does have a score associated with it on the provider's side, but it's not clear if the score is clearly communicated to detectives as well)
THIS DOCUMENT IS NOT A POSITIVE IDENTIFICATION. IT IS AN INVESTIGATIVE LEAD ONLY AND IS NOT PROBABLE CAUSE TO ARRREST. FURTHER INVESTIGATION IS NEEDED TO DEVELOP PROBABLE CAUSE TO ARREST.
Dear god the input image they used to generate that is TERRIBLE! It could be damn near any black male.
The real negligence here is whoever tuned the software to spit out a result for that quality of image rather than a "not enough data, too many matches, please submit a better image" error.
I'm not even sure that's definitely a black man, rather than just any person with some kind of visor or mask. There does seem to be a face in the noise, but human brains are primed to see face shapes.
The deeper reform that needs to happen here is that every person falsely arrested and/or prosecuted needs to be automatically compensated for their time wasted and other harm suffered. Only then will police departments have some incentive for restraint. Currently we have a perverse reverse lottery where if you're unlucky you just lose a day/month/year of your life. With the state of what we're actually protesting I'm not holding my breath (eg the privileged criminals who committed the first degree murder of Breonna Taylor still have yet to be charged), but it's still worth calling out the smaller injustices that criminal "justice" system inflicts.
>The deeper reform that needs to happen here is that every person falsely arrested and/or prosecuted needs to be automatically compensated for their time wasted and other harm suffered.
I agree here, but doing that may lead to the prosecutors trying extra hard to find something to charge a person with after they are arrested, even if it was something trivial that would often go un-prosecuted.
Getting the details right seems tough, but doable.
You're also looking at a scan of a small print out with poor contrast and brightness. There's probably a lot more detail there at full resolution, brightened up to show the face, and then enhanced contrast that the computer is seeing.
> the detectives then put the photo of this man into a "6 pack" photo line-up, from which a store employee then identified that man
This is not correct. The "6-pack" was shown to a security firm's employee, who had viewed the store camera's tape.
"In this case, however, according to the Detroit police report, investigators simply included Mr. Williams’s picture in a “6-pack photo lineup” they created and showed to Ms. Johnston, Shinola’s loss-prevention contractor, and she identified him." [1]
This is why you should be scared of this tech. Computer assisted patsy finder. No need to find the right guy when the ai will happily cough up 20 people nearby who kinda sorta look like the perp enough to stuff them into a lineup in front of a confused and highly fallible witness.
No clue about the likelihood of police using similar facial recognition matches for the rest, but normally the alternates need to be around the same height, build, and complexion as the subject. I would think including multiple potential matches would be a huge no-no simply because your alternates need to be people who you know are not a match. If you just grab the 6 most similar faces and ask the victim to choose, what do you do when they pick the third closest match?
Well you may know some people are not a match because you know where they were, for example pictures could be of people who were incarcerated at the time of the crime.
> But the other side of it is: would police be more hesitant to act on such fuzzy evidence if the top match appeared to be a middle-class Caucasian (i.e. someone who is more likely to take legal recourse)?
Honest question: does race predict legal recourse when decoupled from socioeconomic status, or is this an assumption?
Race and socioeconomic status are deeply intertwined. Or to be more blunt - US society has kept black people poorer. To treat them as independent variables is to ignore the whole history of race in the US.
> To treat them as independent variables is to ignore the whole history of race in the US.
Presumably the coupling of the variables is not binary (dependent or independent) but variable (degrees of coupling). Presumably these variables were more tightly coupled in the past than in the present. Presumably it's useful to understand precisely how coupled these variables are today because it would drive our approach to addressing these disparities. E.g., if the variables are loosely coupled then bias-reducing programs would have a marginal impact on the disparities and the better investment would be social welfare programs (and the inverse is true if the variables are tightly coupled).
>Honest question: does race predict legal recourse when decoupled from socioeconomic status, or is this an assumption?
I think the issue is that regardless of the answer, it isn't decoupled in real world scenarios.
I think the solution isn't dependent upon race either. It is to ensure everyone have access to legal recourse regardless of socioeconomic status. This would have the side effect of benefiting races correlated with lower socioeconomic status more.
> I think the issue is that regardless of the answer, it isn't decoupled in real world scenarios.
Did you think I was asking about non-real-world scenarios? And how do we know that it's coupled (or rather, the degree to which it's coupled) in real world scenarios?
> I think the solution isn't dependent upon race either. It is to ensure everyone have access to legal recourse regardless of socioeconomic status. This would have the side effect of benefiting races correlated with lower socioeconomic status more.
This makes sense to me, although I don't know what this looks like in practice.
Middle class black people often get harassed by police, and there is a long history of far steeper sentences for convictions for drugs used more by the black population (crack) than that used more by the white population (cocaine).
So unequal treatment based on race has quite literally been a feature of the US justice system, independent of socioeconomic status.
Once you are convicted, and are subject to one of the disproportionate sentences often given to black people, nothing short of a major change to how sentencing law works can provide legal recourse. See: https://www.sentencingproject.org/issues/racial-disparity/
If you survive violence at the hands of law enforcement and are not convicted of a crime, or if you don't and your family wants to hold law enforcement accountable, then the first option is to ask the local public prosecutor to pursue criminal charges against your attackers.
Depending on where you live could be a challenge, given the amount of institutional racial bias in the justice system, and how closely prosecutors tend to work with police departments. After all, if prosecutors were going after police brutality cases aggressively, there likely wouldn't be as much of a problem as there is.
If that's fruitless, you would need to seek the help of a civil rights attorney to push your case in the the legal system and/or the media. This is where a lot of higher profile cases like this end up - and often only because they were recorded on video.
> The issue of face recognition algorithms performing worse on dark faces is a major problem.
This needs to be coupled with the truth that people (police) without diverse racial exposure are terrible at identifying people outside of their ethnicity. In the photo/text article they show the top of the "Investigative Lead Report" as an image. You mean to say that every cop who saw the two images side by side did not stop and say "hey, these are not the same person!" They did not, and that's because their own brains' could not see the difference.
This is a major reason police forces need to be ethnically diverse. Just that enables those members of the force who never grew up or spent time outside their ethnicity can learn to tell a diverse range of similar but different people outside their ethnicity apart.
How does computerized facial recognition compare in terms of racial bias and accuracy to human-brain facial recognition? Police are not exactly perfect in either regard.
While also enabling finger-pointing, e.g. the police can say "We aren't racist or aren't at fault. The system is just faulty." while the engineers behind the facial recognition tech can say that they, "Were just doing their job. The police should've heeded their disclaimers, etc."
The difference is that is a known problem, but with ML, a large fraction of the population thinks it's infallible. Worse, its reported confidence for an individual face may be grossly overstated, since that is based on all the data it was trained on, rather than the particular subset you may be dealing with.
large fraction of the population and ML marketing both believe that.
I still think it insane. We have falling crime rates and we still arm ourselves as fast as we can. Humanity could live without face recognition and we wouldn't even suffer any penalties. Nope, people need to sell their evidently shitty ML work.
(1) We still have extreme levels of crime compared to other first world countries even if it is in decline
(2) Your argument strikes me as somewhat similar to "I feel fine why should I keep taking my medicine?". It's not exactly the same as the medicine is scientifically proven to cure disease while it's impossible to measure the impact of police on crime. But "things are getting better so we should change what we're doing" is not a particularly sound logical argument.
Crimes rates dropped even faster in countries with more rehabilitative approaches and long before some countries began to upgrade their police forces because of unrelated fears. It was more about giving people a second chance in all that.
Criminologists aren't certain about surveillance having a positive or negative effects on crime. We have more than 40 studies with mixed results. What is certain with that this kind of surveillance isn't responsible for the falling crime rates described. Most data is from the UK. Currently I don't think countries without surveillance fair worse on crime. Maybe quite to the contrary.
"what we're doing" is not equivalent to increasing video surveillance or generally increasing armament in civil spaces. It may be sound logic if you extend the benefit of the doubt but it may also just be a false statement.
Since surveillance is actually constitutionally forbidden in many countries, on could argue that deployment would "increase crime".
In some other sound logic it might just be a self reinforcing private prison industry with economic interests to keep a steady supply of criminals. Would also be completely sound.
But all these discussions are quite dishonest, don't you think? I just don't want your fucking camera in my face.
> The difference is that is a known problem, but with ML, a large fraction of the population thinks it's infallible.
I don't think anybody actually believes that.
I'm pretty sure the exact opposite is true: People expect AI to fail, because they see it fail all the time in their daily use of computers, for example in voice recognition.
> Worse, its reported confidence for an individual face may be grossly overstated, since that is based on all the data it was trained on, rather than the particular subset you may be dealing with.
At the end of the day, this is still human error. A human compared the faces and decided they looked alike enough to go ahead. The whole thing could've happened without AI, it's just that without AI, processing large volumes of data is infeasible.
I think the human error was made possible because of AI: the AI can search millions of records. The police / detective cannot and will only search a very small set, limiting the search by other means.
The probability of finding an innocent with a similar enough face so that the witness can be fouled is much higher with AI.
It's like asking "is mass surveillance that different from targeted surveillance"?
Yes, of course it is. Orders of magnitude more people could be negatively and undeservedly affected this for no other reason than the fact that it's now cheap enough and easy enough to use by the authorities.
Just to give one example I came up with right now, in the future the police could stop you, take your picture and automatically have it go through its facial recognition database. Kind of like "stop and scan".
Or if the street cameras get powerful enough (and they will), they could take your picture automatically while driving and then stop you.
Think of it like a "TSA system for the roads". A lot more people will be "randomly picked" by these systems from the roads.
Not even close to the same thing. People aren't very reliable witnesses either, But they are pretty good at identifying people they actually know.
It's also poor practice to search a database using a photo or even DNA to go fishing for a suspect. A closest match will generally be found even if the actual perpetrator isn't in the database. I think on some level the authorities know this, which is why they dont seed the databases with their own photos and DNA.
Nope. But It's certainly far more accurate than eyewitnesses. And will reduce the frequency of false positives. Compare this to "suspect is a 6' male approx 200lbs with a white shirt and blue jeans" and then having police frantically pick up everyone in the area that meets this description.
This is the story that gets attention though. Despite it representing an improvement in likely every potential metic you can measure.
The response is what is interesting to me. It triggers a 1984 reflex resulting in people attempting to reject a dramatic enchantment in law enforcement ostensibly because it is not perfect. Or because they believe it a threat to privacy. I think people who are rejecting it should dig deep into their assumptions and reasoning to examine why they are really opposed to technology like this.
> think people who are rejecting it should dig deep into their assumptions and reasoning to examine why they are really opposed to technology like this.
Because a false positive ruins lives? Is that not sufficient? This man’s arrest record is public and won’t disappear. Many employers won’t hire if you have an arrest record (regardless of conviction). His reputation is also permanently smeared. These records are permanently public and in fact some counties publish weekly arrest records on their websites and in newspapers (not that newspapers matter much anymore)
Someday this technology may be better and work more reliably. We’re not there yet. Right now it’s like the early days of voice recognition from the ‘90s.
But as the founders of this country wisely understood, human error is preferable to systematic error. That is the principle under which juries, wildly fallible, exist.
Human error is preferable, even if it is more frequent than the alternative, when it comes to justice. The more human the better.
The suspect said the picture looked nothing like him. When he was shown the picture, he picked up the picture, put it in front of his face, and said "I hope you don't think all black people look alike".
I see this all the time when working with execs. I have to continually remind even very smart people with STEM undergrad and even graduate degrees that a computer vision system cannot magically see things that are invisible to the human eye.
"the computer said so" is way stronger than you would think.
any defence lawyer with more than 3 brain cells would have an absolute field day deconstructing a case brought solely on the basis of a facial recognition. What happened to the idea that police need to gather a variety of evidence confirming their suspicions before an arrest is issued. Even a state prosecutor wouldn't authorize a warrant based on such flimsy methods.
In this particular case, computerized facial recognition is not the problem.
Facial recognition produces potential matches. It's still up to humans to look at footage themselves and use their judgment as to whether it's actually the same person or not, as well as to judge whether other elements fit the suspect or not.
The problem here is 100% on the cop(s) who made that call for themselves, or intentionally ignored obvious differences. (Of course, without us seeing the actual images in question, it's hard to judge.)
There are plenty of dangers with facial recognition (like using it at scale, or to track people without accountability), but this one doesn't seem to be it.
> The problem here is 100% on the cop(s) who made that call for themselves
I disagree. There is plenty of blame on the cops who made that call for themselves, true.
But there doesn't have to be a single party who is at fault. The facial recognition software is badly flawed in this dimension. It's well established that the current technologies are racially biased. So there's at least some fault in the developer of that technology, and the purchasing officer at the police department, and a criminal justice system that allows it to be used that way.
Reducing a complex problem to a single at-fault person produces an analysis that will often let other issues continue to fester. Consider if the FAA always stopped the analysis of air-crashes at: "the pilot made an error, so we won't take any other corrective actions other than punishing the pilot". Air travel wouldn't nearly as safe as it is today.
While we should hold these officers responsible for their mistake (abolish QI so that these officers could be sued civilly for the wrongful arrest!), we should also fix the other parts of the system that are obviously broken.
The cops, the politicians who fund them, the voters who elect the politicians (and possible some of the higher up police ranks), the marketers who sold it to the politician and cops, the management that directed marketing to sell to law enforcement, the developers who let management sell a faulty product, the developers who produced a faulty product.
There's also the company that built the software and marketed it to law enforcement.
Even disregarding the moral hazard of selecting an appropriate training set, the problem is that ML-based techniques are inherently biased. That's the entire point, to boil down a corpus of data into a smaller model that can generate guesses at results. ML is not useful without the bias.
The problem is that bias is OK in some contexts (guessing at letters that a user has drawn on a digitizer) and absolutely wrong in others (needlessly subjecting an innocent person to the judicial system and all of its current flaws). The difference is in four areas, how easily one can correct for false positives/negatives, how easy it is to recognize false output, how the data and results relate to objective reality, and how destructive bad results may be.
When Amazon product suggestions start dumping weird products on me because they think viewing pages is the same as showing interest in the product (vs. guffawing at weird product listings that a Twitter personality has found), the damage is limited. It's just a suggestion that I'm free to ignore. In particularly egregious scenarios, I've had to explain why weird NSFW results were showing up on my screen, but thankfully the person I'm married to trusts me.
When a voice dictation system gets the wrong words for what I am saying, fixing the problem is not hard. I can try again, or I can restart with a different modality.
In both of the previous cases, the ease of detection of false positives is simplified by the fact that I know what the end result should be. These technologies are assistive, not generative. We don't use speech recognition technology to determine what we are attempting to say, we use it to speed up getting to a predetermined outcome.
The product suggestion and dictation issues are annoying when encountering them because they are tied to an objective reality: finding products I want to buy, communicating with another person. They're only "annoying" because the mitigation is simple. Alternatively, you can just dispense with the real world entirely. When a NN "dreams" up pictures of dogs melting into a landscape, that is completely disconnected from any real thing. You can't take the hallucinated dog pictures for anything other than generative art. The purpose of the pictures is to look at the weird results and just say, "ah, that was interesting".
But facial recognition and "depixelization" fails on the first three counts, because they are attempts to reconnect the ML-generated results to a thing that exists in the real world, we don't know what the end results should be, and we (as potential users of the system) don't have any means of adjusting the output or escaping to a different system entirely. And when combined with the purpose of law enforcement, it fails on the fourth aspect, in that the modern judicial system in America is singularly optimized for prosecuting people, not determining innocence or guilt, but getting plea bargain deals out of people. Only 10% criminal cases go to trial. 99% of civil suits end in a settlement rather than a judgement (with 90% of the cases settling before ever going to trial). Even in just this case of the original article, this person and his family have been traumatized, and he has lost at least a full day of productivity, if not much, much more from the associated fallout.
When a company builds and markets a product that harms people, they should be held liable. Due to the very nature of how machine vision and learning techniques work, they'll never be able to address these problems. And the combination of failure in all four categories makes them particularly destructive.
When a company builds and markets a product that harms people, they should be held liable.
They should be, however a company building and marketing a harmful product is a separate issue from cops using specious evidence to arrest a man.
Cops (QI aside), are responsible for the actions they take. They shouldn't be able to hide behind "the tools we use are bad", especially when (as a parent poster said), the tool is known to be bad in the first place and the cops still used it.
> Cops (QI aside), are responsible for the actions they take. They shouldn't be able to hide behind "the tools we use are bad", especially when (as a parent poster said), the tool is known to be bad in the first place and the cops still used it.
But literally no one in this thread is arguing to not hold them responsible.
Everyone agrees that yes, the cops and PD are responsible. It's just that some people are arguing that there are other parties that also bear responsibility.
No one thinks the cops should be able to hide behind the fact that the tool is bad. I think these cops should be fired, sued for a wrongful arrest. I think QI should be abolished so wronged party can go after the house of the officer that made the arrest in a civil court. I think the department should be on the hook for a large settlement payment.
But I also think the criminal justice system should enjoin future departments from using this known bad technology. I think we should also be mad at the technology vendors that created this bad tool.
I guess the argument would be that some companies are pushing- actively selling- the technology to PDs.
My experience listening to the sales pitch by our sales team - of tech I helped develop; they would not only ignore the caveats attached to the products by engineering but straight out sell features that were not done, not even in the roadmap, or just physically impossible to implement as sold.
With that in mind I can see how the companies selling these solutions are responsible as well.
Sure, and that was one of the parties I listed as being at fault:
> purchasing officer at the police department
However, if the criminal justice system decides that this is an acceptable use of software, then the criminal justice system itself also bears responsibility.
The developer of the software also bears the responsibility for developing, marketing, and selling the software for the police department.
I agree that the PD bears the majority of the culpability here, but I disagree that it bears every ounce of fault that could exist in this scenario.
The justification for depriving someone of their liberty lies solely with the arresting officer. They can base that on whatever they want, as long as they can later justify it to a court.
For example, you might have a trusted informant who could tell you who committed a local burglary, just this on its own could be legitimate grounds to make an arrest. The same informant might walk into a police station and tell the same information to someone else, for that officer, it might not be sufficient to justify an arrest.
Third, Robert’s arrest demonstrates why claims that face recognition isn’t dangerous are far-removed from reality. Law enforcement has claimed that face recognition technology is only used as an investigative lead and not as the sole basis for arrest. But once the technology falsely identified Robert, there was no real investigation.
I fear this is going to be the norm among police investigations.
> Federal studies have shown that facial-recognition systems misidentify Asian and black people up to 100 times more often than white people.
The idea behind inclusion is that this product would have never made it to production if the engineering teams, product team, executive team and board members represented the population. But enough representation so that there is a countering voice is even better.
Would have just been "this edge case is not an edge case at all, axe it."
Accurately addressing a market is the point of the corporation more than an illusion of meritocracy amongst the employees.
This is so incredibly common, it's embarrassing. I was on an expert panel about "AI and Machine Learning in Healthcare and Life Sciences" back in January, and I made it a point throughout my discussions to keep emphasizing the amount of bias inherent in our current systems, which ends up getting amplified and codified in machine learning systems. Worse yet, it ends up justifying the bias based on the false pretense that the systems built are objective and the data doesn't lie.
Afterward, a couple people asked me to put together a list of the examples I cited in my talk. I'll be adding this to my list of examples:
* A hospital AI algorithm discriminating against black people when providing additional healthcare outreach by amplifying racism already in the system. https://www.nature.com/articles/d41586-019-03228-6
* Misdiagnosing people of African decent with genomic variants misclassified as pathogenic due to most of our reference data coming from European/white males. https://www.nejm.org/doi/full/10.1056/NEJMsa1507092
* When AI criminal risk prediction software used by judges in deciding the severity of punishment for those convicted predicts a higher chance of future offence for a young, black first time offender than for an older white repeat felon. https://www.propublica.org/article/machine-bias-risk-assessm...
I agree 100% about how common it is. The industry also pays lip service about doing something about it. My last job was at a research institution and we had a data ethics czar, who's a very smart (Stats phd) guy and someone I consider a friend. A lot of his job was to go around the org and conferences talking about things like this.
While there's a lot of head nodding, nothing is ever actually addressed in day to day operations. Data scientists barely know what's going on when they throw things through TensorFlow. What matters is the outcome and the confusion matrix at the end.
I say this as someone who works in data and implements AI/ML platforms. Mr. Williams needs to find the biggest ambulance chasing lawyer and file civil suits not only the law enforcement agencies involved, but top down everyone at DataWorks from the president to the data scientist to the lowly engineer who put this in production.
These people have the power to ruin lives. They need to be made an example of and held accountable for the quality of their work.
>When AI criminal risk prediction software used by judges in deciding the severity of punishment for those convicted predicts a higher chance of future offence for a young, black first time offender than for an older white repeat felon.
>When Amazon's AI recruiting tool inadvertantly filtered out resumes from women
>When Google's hate speech detecting AI inadvertantly censored anyone who used vernacular referred to in this article as being "African American English
There's simply no indication that these aren't statistically valid priors. And we have mountains of scientific evidence to the contrary, but if dared post anything (cited, published literature) I'd be banned. This is all based on the unfounded conflation between equality of outcome and equality of opportunity, and the erasure of evidence of genes and culture playing a role in behavior and life outcomes.
> There's simply no indication that these aren't statistically valid priors. And we have mountains of scientific evidence to the contrary, but if dared post anything (cited, published literature) I'd be banned.
I'd consider reading the sources I posted in my comment before responding with ill-conceived notions. Literally every single example I posted linked to the peer-reviewed scientific evidence (cited, published literature) indicating the points I summarized.
The only link I posted without peer-reviewed literature was the last one with the positive outcome, and that's the one I commented had suspect analysis.
Let's just consider an example; where do you draw the line in the following list? To avoid sending travelers through unsafe areas:
1. Google's routing algorithm is conditioned on demographics
2. Google's routing algorithm is conditioned on income/wealth
3. Google's routing algorithm is conditioned on crime density
4. Google's routing algorithm cannot condition on anything that would disproportionately route users away from minority neighborhoods
I think the rational choice, to avoid forcing other people to take risks that they may object to, is somewhere between 2 and 3. But the current social zeitgeist seems only to allow for option four, since an optimally sampled dataset will have very strong correlations between 1-3, to the point that in most parts of the us they would all result in the same routing bias.
This is exactly why I suggested actually reading the sources I posted before responding. The Google example has nothing to do with routing travelers. It was an algorithm designed to detect sentiment in online comments and to auto-delete any comments that were classified as hate-speech. The problem was that it mis-classified entire dialects of English (meaning it completely failed at determining sentiment for certain people), deleting all comments from the people of certain cultures (unfairly, disproportionately censoring a group of people). That's the dictionary definition of bias.
You're completely missing my point. And the purpose of my hypothetical. So let me try it with your example:
>The problem was that it mis-classified entire dialects of English (meaning it completely failed at determining sentiment for certain people), deleting all comments from the people of certain cultures
What happens in the case that a particular culture is more hateful? Do we just disregard any data that indicates socially unacceptable bias?
> What happens in the case that a particular culture is more hateful? Do we just disregard any data that indicates socially unacceptable bias?
That's not what was happening. If you read the link, you'll see the problem is that the AI/ML system was mis-classifying non-hateful speech as hateful, just because of the dialect being used.
If it were the case that the culture was more hateful, then it wouldn't have been considered "mis-classification."
> You're completely missing my point.
I'm not missing your point; it's just not a well-reasoned or substantiated point. Here were your points:
> There's simply no indication that these aren't statistically valid priors.
We do have every indication that this wasn't what was happening in literally every single example I posted. You just have to read them.
> And we have mountains of scientific evidence to the contrary, but if dared post anything (cited, published literature) I'd be banned.
You say that, and yet you keep posting your point without any evidence whatsoever. Meanwhile, every single example I posted did cite peer-reviewed, published scientific evidence.
> This is all based on the unfounded conflation between equality of outcome and equality of opportunity, and the erasure of evidence of genes and culture playing a role in behavior and life outcomes.
Again, peer-reviewed published literature disagrees. Reading it explains why the point that it's all unfounded conflation is incorrect.
I think that your prints, DNA, and so forth must be, in the interests of fairness, utterly erased from all systems in the case of false arrest. With some kind of enormous, ruinous financial penalty in place for the organizations for non-compliance, as well as automatic jail times for involved personnel. These things need teeth to happen.
He wasn't arrested until the shop owner had also "identified" him. The cops used a single frame of grainy video to pull his driver's license photo, and then put that photo in a lineup and showed the store clerk.
The store clerk (who hadn't witnessed the crime and was going off the same frame of video fed into the facial recognition software) said the driver's license photo was a match.
There are several problems with the conduct of the police in this story but IMHO the use of facial recognition is not the most egregious.
Yes, this is a story of police misconduct. The regulation of facial recognition that is required is regulation against police/authority stupidity. The FR system aids in throwing away misses, leaving investigative leads. But if a criminal is not in the FR database to begin with, any results of the FR are wastes of time.
"The Shinola shoplifting occurred in October 2018. Katherine Johnston, an investigator at Mackinac Partners, a loss prevention firm, reviewed the store’s surveillance video and sent a copy to the Detroit police"
"In this case, however, according to the Detroit police report, investigators simply included Mr. Williams’s picture in a “6-pack photo lineup” they created and showed to Ms. Johnston, Shinola’s loss-prevention contractor, and she identified him. (Ms. Johnston declined to comment.)"
I think you're correct that the person was not an owner or clerk. IMHO the salient point is that the person was not any sort of eyewitness but merely comparing the same grainy photo as the algorithm.
More importantly, the person wasn't an eyewitness, and the 6-pack photo array was window dressing to make the outside technician appear to be an eyewitness.
The story is the same one that all anti-surveillance, anti-police militarization, pro-privacy, and anti-authoritarian people foretell. Good technology will be used enable, amplify, and justify civil rights abuses by authority figures from your local beat cop, to a faceless corporation, a milquetoast public servant, or the president of the United States.
Our institutions and systems (and maybe humans in general) are not robust enough to cleanly handle these powers, and we are making the same mistake over and over and over again.
Correct, and this has been the story with every piece of technology or tool we've ever given to police. We give them body cameras and they're turned off or used to create FPS-style snuff films of gunned down citizens. Give them rubber bullets and they're aimed at protesters eyeballs. Give them tasers and they're used as an excuse to shoot someone when the suspect "resists." Give them flashbangs and they'll throw them into an infant's crib. Give them mace and it's used out of car windows to punish journalists for standing on the sidewalks.
The mistake is to treat any police department as a good-faith participant in the goal of reducing police violence. Any tool you give them will be used to brutalize. The only solution is to give them less.
Another reason that it's absolutely insane that the state demands to know where you sleep at night in a free society. These clowns were able to just show up at his house and kidnap him.
The practice of disclosing one's residence address to the state (for sale to data brokers[1] and accessible by stalkers and the like) when these kinds of abuses are happening is something that needs to stop. There's absolutely no reason that an ID should be gated on the state knowing your residence. It's none of their business. (It's not on a passport. Why is it on a driver's license?)
> Even if this technology does become accurate (at the expense of people like me), I don’t want my daughters’ faces to be part of some government database.
Stop using Amazon Ring and similar doorbell products.
And then in some states employers are allowed to ask have you eve been arrested (never mind convicted of any crime) on employment application. Sure, keep putting people down. One day it might catch up with China's social scoring policies.
I've been thinking this sort of event has become inevitable. Tech development and business models support extending the environments in which we collect images and analyze them. Confidence values lead to statistical guilt. I wrote about it here if interested: https://unintendedconsequenc.es/inevitable-surveillance/
Unlikely unless they were compelled by law or found something else to replace it, and I think it's the latter. Something about machine learning and such.
> "I picked it up and held it to my face and told him, 'I hope you don't think all Black people look alike,' " Williams said.
I'm white. I grew up around a sea of white faces. Often when watching a movie filled with a cast of non-white faces, I will have trouble distinguishing one actor from another, especially if they are dressed similarly. This sometimes happens in movies with faces similar to the kinds I grew up surrounded by, but less so.
So unfortunately, yes, I probably do have more trouble distinguishing one black face from another vs one white face from another.
This is known as the cross-race effect and it's only something I became aware of in the last 5-10 years.
Add to that the fallibility of human memory, and I can't believe we still even use line ups. Are there any studies about how often line ups identify the wrong person?
I lived in South Africa for a while and heard many times, with various degrees of irony, "you white people all look the same" from black South Africans. So yeah it's definitely a cross-racial recognition problem, and it's probably also a problem with distinguishing between members of visible minorities using traits beyond the most noticable othering characteristic.
There is just so much wrong with this story. For starters:
The shoplifting incident occurred in October 2018 but it wasn’t until March 2019 that the police uploaded the security camera images to the state image-recognition system but the police still waited until the following January to arrest Williams. Unless there was something special about that date in October, there is no way for anyone to remember what they might have been doing on a particular day 15 months previously. Though, as it turns out, the NPR report states that the police did not even try to ascertain whether or not he had an alibi.
Also, after 15 months, there is virtually no chance that any eye-witness (such as the security guard who picked Williams out of a line-up) would be able to recall what the suspect looked like with any degree of certainty or accuracy.
This WUSF article [1] includes a photo of the actual “Investigative Lead Report” and the original image is far too dark for a anyone (human or algorithm) to recognise the person. It’s possible that the original is better quality and better detail can be discerned by applying image-processing filters – but it still looks like a very noisy source.
That same “Investigative Lead Report” also clearly states that “This document is not a positive identification … and is not probable cause to arrest. Further investigation is needed to develop probable cause of arrest”.
The New York Times article [2] states that this facial recognition technology that the Michigan tax-payer has paid millions of dollars for is known to be biased and that the vendors do “not formally measure the systems’ accuracy or bias”.
Finally, the original NPR article states that
> "Most of the time, people who are arrested using face recognition are not told face recognition was used to arrest them," said Jameson Spivack
It gets even crazier to think about when you realise that cities like Detroit are overwhelmingly Black. The police there are just not providing good value for the people who live there.
This is a classic example of the false positive rate fallacy.
Let's say that there are a million people, and the police have photos of 100,000 of them. A crime is committed, and they pull the surveillance of it, and match against their database. They have a funky image matching system that has a false positive rate of 1 in 100,000 people, which is way more accurate than I think facial recognition systems are right now, but let's just roll with it. Of course, on average, this system will produce one positive hit per search. So, the police roll up to that person's home and arrest them.
Then, in court, they get to argue that their system has a 1 in 100,000 false positive rate, so there is a chance of 1 in 100,000 that this person is innocent.
Wrong!
There are ten people in the population of 1 million that the software would comfortably produce a positive hit for. They can't all be the culprit. The chance isn't 1 in 100,000 that the person is innocent - it is in fact at least 9 out of 10 that they are innocent. This person just happens to be the one person out of the ten that would match that had the bad luck to be stored in the police database. Nothing more.
There's a good book called "The Drunkards Walk", that describes a woman who was jailed after having 2 children die from SIDS. They argued that the odds of this happening is 1 in a million (or something like that), so probably the woman is a baby killer. The prosecution had statisticians argue this. The woman was found guilty.
She later won on appeal in part because the defense showed that the testimony and argument of the original statisticians were wrong.
This stuff is so easy to get wrong. A little knowledge of statistics can be dangerous.
Sure.. But the case being discussed has a maximum frequency of 1 in a million every 18 (2 terms of childbirth) months, further reduced by needing to be a woman of reproductive age, fertile, etc etc.
This case of "one in a million" does not happen frequently.
If I'm searching for a murderer in a town of 1000, it takes about 10 independent bits of evidence to get the right one. And when I charge someone, I must already have the vast majority of that evidence. To say "oh well we don't know that it wasn't Mr. or Mrs. Doe, let's bring them in" is itself a breach of the Does' rights. I'm ignoring 9 of the 10 bits of evidence!
Using a low-accuracy facial recognition system and a low-accountability lineup procedure to elevate some random man who did nothing wrong from presumed-innocent to 1-in-6 to prime suspect, without having the necessary amount of evidence, is committing the exact same error and is nearly as egregious as pulling a random civilian out of a hat and charging them.
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[ 4.7 ms ] story [ 320 ms ] threadIf only their elected officials would listen to them...
* Revenue from tickets/fines/etc. shouldn't go to into police pockets or it just incentives rent seeking behavior by people with law enforcement powers
* Settlements should come out of their budget not city insurance.
It would be a great result if a court declared that the use of racially biased facial recognition software is a violation of the 14th amendment violation, and enjoined PDs from using such software unless it can be demonstrated to be free of racial bias.
Fortunately, that is changing, but not that quickly.
Maybe he can go after the makers of the facial recognition software, but they can probably point their finger at the cops for using it wrong.
So, in any case, the guy will be left with a big legal bill.
And here's a 1st-person account from the arrested man: https://www.washingtonpost.com/opinions/2020/06/24/i-was-wro...
I just did. 3 minutes wasn't that bad and I wasn't somewhere where it would be a problem.
> Why do sites do this?
NPR is a radio network. I have seen that often they do transcribe their clips. I am not sure what the process they have for that looks like, but it seems this particular clip didn't get transcribed.
Edit: looks like they do have a transcription mentioned elsewhere in the thread. So seems like some kind of UI fail.
edit: looks like there's a text version of the article. I'm assuming this is a CMS issue: there's an audio story and a "print story", but the former hadn't been linked to the latter: https://news.ycombinator.com/item?id=23628790
https://www.automaticsync.com/captionsync/what-qualifies-as-... (see section: "High Quality Captioning")
I am not aware of many TV shows that offer audio commentary for the visually impaired.
Here is an example of one that does.
https://www.npr.org/2015/04/18/400590705/after-fan-pressure-...
https://text.npr.org/s.php?sId=882683463
The linked story is audio only and is associated with the Morning Edition broadcast, but the full story appears under our Special Series section.
(I work for NPR)
Of course really I blame the AI/ML hucksters for part of this mess who have sold us the idea of machines replacing rather than augmenting human decision making.
Having a picture or just a description of the face is one of the most important pieces of information the police has in order to do actual policing. You can be arrested for just broadly matching the description if you happen to be in the vicinity.
Had the guy been convicted of anything just based on that evidence, this would be a scandal. As it is, a suspect is just a suspect and this kind of thing happens all the time, because humans are just as fallible. It's just not news when there's no AI involved.
Faces generated by AI means should not count as 'probable cause' to go and arrest people. They should count as fantasy.
They don't:
https://wfdd-live.s3.amazonaws.com/styles/story-full/s3/imag...
There was further work involved, there was a witness who identified the man on a photo lineup, and so on. The AI did not identify anyone, it gave a "best effort" match. All the actual mistakes were made by humans.
But no, its AI, its magical and it must be right.
The current best practice is to have a witness pick out the suspect from 6 photos. It should be immediately obvious that right off the bat there's a 17% chance of the witness randomly picking the "right" person. It's a terrible way to do things and it's no surprise that people are wrongly convicted again and again on eyewitness testimony.
https://en.wikipedia.org/wiki/Capital_Gazette_shooting
At what point can we decide that people in positions of power are not and will not ever be responsible enough to handle this technology?
Surely as a society we shouldn’t continue to naively assume that police are “responsible” like we’ve assumed in the past?
Of course we shouldn't assume it, but we absolutely should require it.
Uncertainty is a core part of policing which can't be removed.
> Authorities said he was not carrying identification at the time of his arrest and was not cooperating. … an issue with the fingerprint machine ultimately made it difficult to identify the suspect, … A source said officials used facial recognition technology to confirm his identity.
https://en.wikipedia.org/wiki/Capital_Gazette_shooting#Suspe...
> Police, who arrived at the scene within a minute of the reported gunfire, apprehended a gunman found hiding under a desk in the newsroom, according to the top official in Anne Arundel County, where the attack occurred.
https://www.washingtonpost.com/local/public-safety/heavy-pol...
This doesn't really seem like an awesome use of facial recognition to me. He was already in custody after getting picked up at the crime scene. I doubt he would have been released if facial recognition didn't exist.
The technology is certainly not robust enough to be trusted to work correctly at that level yet. Even if it was improved I think there is a huge moral issue with the police having the power to use it indiscriminately on the street.
The issue of face recognition algorithms performing worse on dark faces is a major problem. But the other side of it is: would police be more hesitant to act on such fuzzy evidence if the top match appeared to be a middle-class Caucasian (i.e. someone who is more likely to take legal recourse)?
What's hilarious is that it makes faces that look nothing like the original high-resolution images.
https://twitter.com/Chicken3gg/status/1274314622447820801
[1] https://github.com/NVlabs/stylegan [2] https://arxiv.org/pdf/2003.03808.pdf (ctrl+f ffhq)
I think there's genuine cause for concern here, especially if technologies like these are candidates for inclusion in any real law enforcement decision-making.
In this case, it's incumbent on the software vendors to ensure that less-than-certain results aren't even shown to the user. American police can't generally be trusted to understand nuance and/or do the right thing.
Yes.
> Does it return a list (sorted by confidence) of possible suspects,
Yes.
> ... or any other kind of feedback that would indicate even to a layperson how much uncertainty there is?
Yes it does. It also states in large print heading “THIS DOCUMENT IS NOT A POSITIVE IDENTIFICATION IT IS AN INVESTIGATIVE LEAD AND IS NOT PROBABLE CAUSE TO ARREST”.
You can see a picture of this in the ACLU article.
The police bungled this badly by setting up a fake photo lineup with the loss prevention clerk who submitted the report (who had only ever seen the same footage they had).
However, tools that are rife for misuse do not get a pass because they include a bold disclaimer. If the tool/process can not prevent misuse, the tool/process is broken and possibly dangerous.
That said, we have little data on how often the tool results in catching dangerous criminals versus how often it misidentifies innocent people. We have little data on if those innocent people tend to skew toward a particular demographic.
But I have a fair suspicion that dragnet techniques like this unfortunately can be both effective and also problematic.
> The police bungled this badly by setting up a fake photo lineup...*
FWIW, this process is similar to traditional police lineups. The witness is shown 4-6 people – one who is the actual suspect, and several that vaguely match a description of the suspect. When I was asked to identify a suspect in my robbery, the lineup included an assistant attorney who would later end up prosecuting the case. The police had to go out and find tall slight-skinned men to round out the lineup.
> ... with the loss prevention clerk who submitted the report (who had only ever seen the same footage they had).
Yeah, I would hope that this is not standard process. The lineup process is already imperfect and flawed as it is even with a witness who at least saw the crime first-hand.
Essentially, an employee of the facial recognition provider forwarded an "investigative lead" for the match they generated (which does have a score associated with it on the provider's side, but it's not clear if the score is clearly communicated to detectives as well), and the detectives then put the photo of this man into a "6 pack" photo line-up, from which a store employee then identified that man as being the suspect.
Everyone involved will probably point fingers at each other, because the provider for example put large heading on their communication that, "this is not probable cause for an arrest, this is only an investigative lead, etc.", while the detectives will say well we got a hit from a line-up, blame the witness, and the witness would probably say well the detectives showed me a line-up and he seemed like the right guy (or maybe as is often the case with line-ups, the detectives can exert a huge amount of bias/influence over witnesses).
EDIT: Just to be clear, none of this is to say that the process worked well or that I condone this. I think the data, the technology, the processes, and the level of understanding on the side of the police are all insufficient, and I do not support how this played out, but I think it is easy enough to provide at least some pseudo-justification at each step along the way.
Presumably, the facial recognition software would provide an additional filter/sort. But at least in my situation, I could actually see how big the total pool of potential matches and thus have a sense of uncertainty about false positives, even if I were completely ignorant about the impact of false negatives (i.e. what if my suspect didn't live within x-miles of the scene, or wasn't a known/convicted felon?)
So the caution re: face recognition software is how it may non-transparently add confidence to this already very imperfect filtering process.
(in my case, the suspect was eventually found because he had committed a number of robberies, including being clearly caught on camera, and in an area/pattern that was easy to narrow down where he operated)
> The detective turned over the first piece of paper. It was a still image from a surveillance video, showing a heavyset man, dressed in black and wearing a red St. Louis Cardinals cap, standing in front of a watch display. Five timepieces, worth $3,800, were shoplifted.
> “Is this you?” asked the detective.
> The second piece of paper was a close-up. The photo was blurry, but it was clearly not Mr. Williams. He picked up the image and held it next to his face.
All the preceding grafs are told in the context of "this what Mr. Williams said happened", most explicitly this one:
> “When’s the last time you went to a Shinola store?” one of the detectives asked, in Mr. Williams’s recollection.
According to the ACLU complaint, the DPD and prosecutor have refused FOIA requests regarding the case:
https://www.aclu.org/letter/aclu-michigan-complaint-re-use-f...
> Yet DPD has failed entirely to respond to Mr. Williams’ FOIA request. The Wayne County Prosecutor also has not provided documents.
This arrest happened 6 months ago. Who else besides the suspect and the police do you believe reporters should ask for "basic corroboration" of events that took place inside a police station? Or do you think this story shouldn't be reported on at all until the police agree to give additional info?
NPR is unfortunately an actor in the current culture war. The subject of the story is a POC, therefore by definition innocent victim of "systemic racism", "white supremacy", "implicit bias" or whatever fighting word the left comes up.
Check any past story where the subject of the story is a POC, those are never in any way responsible for their own misfortunes.
If in the current story time will show that the investigative lead was indeed correct to point Mr Williams you won't ever see a retraction or update on NPR.
Yes, it's called forwarding a narrative as opposed to reporting on objective facts.
The 4th sentence says: "Detectives zoomed in on the grainy footage..."
They are in the business of exposing you to as many paid ads as possible. And they believe providing outgoing links reduces their ability to do that.
NPR is a non-profit that is mostly funded by donations. They only have minimal paid ads on their website to pay for running costs - they could easily optimize the news pages to increase ad revenue but they don't because it would get in the way of their goals.
This is absurdly dangerously. The AI will find people who look like the suspect, that’s how the technology works. A lineup as evidence will almost guarantee a bad outcome, because of course the man looks like the suspect!
But for a photo lineup I can't imagine why you don't have least 25 photos to pick from.
This is the lead provided:
https://wfdd-live.s3.amazonaws.com/styles/story-full/s3/imag...
Note that it says in red and bold emphasis:
THIS DOCUMENT IS NOT A POSITIVE IDENTIFICATION. IT IS AN INVESTIGATIVE LEAD ONLY AND IS NOT PROBABLE CAUSE TO ARRREST. FURTHER INVESTIGATION IS NEEDED TO DEVELOP PROBABLE CAUSE TO ARREST.
The real negligence here is whoever tuned the software to spit out a result for that quality of image rather than a "not enough data, too many matches, please submit a better image" error.
The deeper reform that needs to happen here is that every person falsely arrested and/or prosecuted needs to be automatically compensated for their time wasted and other harm suffered. Only then will police departments have some incentive for restraint. Currently we have a perverse reverse lottery where if you're unlucky you just lose a day/month/year of your life. With the state of what we're actually protesting I'm not holding my breath (eg the privileged criminals who committed the first degree murder of Breonna Taylor still have yet to be charged), but it's still worth calling out the smaller injustices that criminal "justice" system inflicts.
I agree here, but doing that may lead to the prosecutors trying extra hard to find something to charge a person with after they are arrested, even if it was something trivial that would often go un-prosecuted.
Getting the details right seems tough, but doable.
that's what happens if you're lucky
This is not correct. The "6-pack" was shown to a security firm's employee, who had viewed the store camera's tape.
"In this case, however, according to the Detroit police report, investigators simply included Mr. Williams’s picture in a “6-pack photo lineup” they created and showed to Ms. Johnston, Shinola’s loss-prevention contractor, and she identified him." [1]
[1] ibid.
How did the people in the 6 pack photo line-up match up against the facial recognition? Were they likely matches?
Honest question: does race predict legal recourse when decoupled from socioeconomic status, or is this an assumption?
Presumably the coupling of the variables is not binary (dependent or independent) but variable (degrees of coupling). Presumably these variables were more tightly coupled in the past than in the present. Presumably it's useful to understand precisely how coupled these variables are today because it would drive our approach to addressing these disparities. E.g., if the variables are loosely coupled then bias-reducing programs would have a marginal impact on the disparities and the better investment would be social welfare programs (and the inverse is true if the variables are tightly coupled).
I think the issue is that regardless of the answer, it isn't decoupled in real world scenarios.
I think the solution isn't dependent upon race either. It is to ensure everyone have access to legal recourse regardless of socioeconomic status. This would have the side effect of benefiting races correlated with lower socioeconomic status more.
Did you think I was asking about non-real-world scenarios? And how do we know that it's coupled (or rather, the degree to which it's coupled) in real world scenarios?
> I think the solution isn't dependent upon race either. It is to ensure everyone have access to legal recourse regardless of socioeconomic status. This would have the side effect of benefiting races correlated with lower socioeconomic status more.
This makes sense to me, although I don't know what this looks like in practice.
So unequal treatment based on race has quite literally been a feature of the US justice system, independent of socioeconomic status.
If you survive violence at the hands of law enforcement and are not convicted of a crime, or if you don't and your family wants to hold law enforcement accountable, then the first option is to ask the local public prosecutor to pursue criminal charges against your attackers.
Depending on where you live could be a challenge, given the amount of institutional racial bias in the justice system, and how closely prosecutors tend to work with police departments. After all, if prosecutors were going after police brutality cases aggressively, there likely wouldn't be as much of a problem as there is.
If that's fruitless, you would need to seek the help of a civil rights attorney to push your case in the the legal system and/or the media. This is where a lot of higher profile cases like this end up - and often only because they were recorded on video.
This needs to be coupled with the truth that people (police) without diverse racial exposure are terrible at identifying people outside of their ethnicity. In the photo/text article they show the top of the "Investigative Lead Report" as an image. You mean to say that every cop who saw the two images side by side did not stop and say "hey, these are not the same person!" They did not, and that's because their own brains' could not see the difference.
This is a major reason police forces need to be ethnically diverse. Just that enables those members of the force who never grew up or spent time outside their ethnicity can learn to tell a diverse range of similar but different people outside their ethnicity apart.
I still think it insane. We have falling crime rates and we still arm ourselves as fast as we can. Humanity could live without face recognition and we wouldn't even suffer any penalties. Nope, people need to sell their evidently shitty ML work.
(2) Your argument strikes me as somewhat similar to "I feel fine why should I keep taking my medicine?". It's not exactly the same as the medicine is scientifically proven to cure disease while it's impossible to measure the impact of police on crime. But "things are getting better so we should change what we're doing" is not a particularly sound logical argument.
Criminologists aren't certain about surveillance having a positive or negative effects on crime. We have more than 40 studies with mixed results. What is certain with that this kind of surveillance isn't responsible for the falling crime rates described. Most data is from the UK. Currently I don't think countries without surveillance fair worse on crime. Maybe quite to the contrary.
"what we're doing" is not equivalent to increasing video surveillance or generally increasing armament in civil spaces. It may be sound logic if you extend the benefit of the doubt but it may also just be a false statement.
Since surveillance is actually constitutionally forbidden in many countries, on could argue that deployment would "increase crime".
In some other sound logic it might just be a self reinforcing private prison industry with economic interests to keep a steady supply of criminals. Would also be completely sound.
But all these discussions are quite dishonest, don't you think? I just don't want your fucking camera in my face.
I don't think anybody actually believes that.
I'm pretty sure the exact opposite is true: People expect AI to fail, because they see it fail all the time in their daily use of computers, for example in voice recognition.
> Worse, its reported confidence for an individual face may be grossly overstated, since that is based on all the data it was trained on, rather than the particular subset you may be dealing with.
At the end of the day, this is still human error. A human compared the faces and decided they looked alike enough to go ahead. The whole thing could've happened without AI, it's just that without AI, processing large volumes of data is infeasible.
The probability of finding an innocent with a similar enough face so that the witness can be fouled is much higher with AI.
A computer can make a mistake across literally any person who has a publicly available photo (which is almost everyone).
Also, the facial recognition technologies are provably extremely racially biased.
Yes, of course it is. Orders of magnitude more people could be negatively and undeservedly affected this for no other reason than the fact that it's now cheap enough and easy enough to use by the authorities.
Just to give one example I came up with right now, in the future the police could stop you, take your picture and automatically have it go through its facial recognition database. Kind of like "stop and scan".
Or if the street cameras get powerful enough (and they will), they could take your picture automatically while driving and then stop you.
Think of it like a "TSA system for the roads". A lot more people will be "randomly picked" by these systems from the roads.
It's also poor practice to search a database using a photo or even DNA to go fishing for a suspect. A closest match will generally be found even if the actual perpetrator isn't in the database. I think on some level the authorities know this, which is why they dont seed the databases with their own photos and DNA.
This is the story that gets attention though. Despite it representing an improvement in likely every potential metic you can measure.
The response is what is interesting to me. It triggers a 1984 reflex resulting in people attempting to reject a dramatic enchantment in law enforcement ostensibly because it is not perfect. Or because they believe it a threat to privacy. I think people who are rejecting it should dig deep into their assumptions and reasoning to examine why they are really opposed to technology like this.
Because a false positive ruins lives? Is that not sufficient? This man’s arrest record is public and won’t disappear. Many employers won’t hire if you have an arrest record (regardless of conviction). His reputation is also permanently smeared. These records are permanently public and in fact some counties publish weekly arrest records on their websites and in newspapers (not that newspapers matter much anymore)
Someday this technology may be better and work more reliably. We’re not there yet. Right now it’s like the early days of voice recognition from the ‘90s.
Human error is preferable, even if it is more frequent than the alternative, when it comes to justice. The more human the better.
Humans can be held accountable.
I see this all the time when working with execs. I have to continually remind even very smart people with STEM undergrad and even graduate degrees that a computer vision system cannot magically see things that are invisible to the human eye.
"the computer said so" is way stronger than you would think.
I'll be watching this case with great interest
Facial recognition produces potential matches. It's still up to humans to look at footage themselves and use their judgment as to whether it's actually the same person or not, as well as to judge whether other elements fit the suspect or not.
The problem here is 100% on the cop(s) who made that call for themselves, or intentionally ignored obvious differences. (Of course, without us seeing the actual images in question, it's hard to judge.)
There are plenty of dangers with facial recognition (like using it at scale, or to track people without accountability), but this one doesn't seem to be it.
I disagree. There is plenty of blame on the cops who made that call for themselves, true.
But there doesn't have to be a single party who is at fault. The facial recognition software is badly flawed in this dimension. It's well established that the current technologies are racially biased. So there's at least some fault in the developer of that technology, and the purchasing officer at the police department, and a criminal justice system that allows it to be used that way.
Reducing a complex problem to a single at-fault person produces an analysis that will often let other issues continue to fester. Consider if the FAA always stopped the analysis of air-crashes at: "the pilot made an error, so we won't take any other corrective actions other than punishing the pilot". Air travel wouldn't nearly as safe as it is today.
While we should hold these officers responsible for their mistake (abolish QI so that these officers could be sued civilly for the wrongful arrest!), we should also fix the other parts of the system that are obviously broken.
Who decided to use this software for this purpose, despite these bad flaws and well established bias? The buck stops with the cops.
Plenty of blame to go around.
Even disregarding the moral hazard of selecting an appropriate training set, the problem is that ML-based techniques are inherently biased. That's the entire point, to boil down a corpus of data into a smaller model that can generate guesses at results. ML is not useful without the bias.
The problem is that bias is OK in some contexts (guessing at letters that a user has drawn on a digitizer) and absolutely wrong in others (needlessly subjecting an innocent person to the judicial system and all of its current flaws). The difference is in four areas, how easily one can correct for false positives/negatives, how easy it is to recognize false output, how the data and results relate to objective reality, and how destructive bad results may be.
When Amazon product suggestions start dumping weird products on me because they think viewing pages is the same as showing interest in the product (vs. guffawing at weird product listings that a Twitter personality has found), the damage is limited. It's just a suggestion that I'm free to ignore. In particularly egregious scenarios, I've had to explain why weird NSFW results were showing up on my screen, but thankfully the person I'm married to trusts me.
When a voice dictation system gets the wrong words for what I am saying, fixing the problem is not hard. I can try again, or I can restart with a different modality.
In both of the previous cases, the ease of detection of false positives is simplified by the fact that I know what the end result should be. These technologies are assistive, not generative. We don't use speech recognition technology to determine what we are attempting to say, we use it to speed up getting to a predetermined outcome.
The product suggestion and dictation issues are annoying when encountering them because they are tied to an objective reality: finding products I want to buy, communicating with another person. They're only "annoying" because the mitigation is simple. Alternatively, you can just dispense with the real world entirely. When a NN "dreams" up pictures of dogs melting into a landscape, that is completely disconnected from any real thing. You can't take the hallucinated dog pictures for anything other than generative art. The purpose of the pictures is to look at the weird results and just say, "ah, that was interesting".
But facial recognition and "depixelization" fails on the first three counts, because they are attempts to reconnect the ML-generated results to a thing that exists in the real world, we don't know what the end results should be, and we (as potential users of the system) don't have any means of adjusting the output or escaping to a different system entirely. And when combined with the purpose of law enforcement, it fails on the fourth aspect, in that the modern judicial system in America is singularly optimized for prosecuting people, not determining innocence or guilt, but getting plea bargain deals out of people. Only 10% criminal cases go to trial. 99% of civil suits end in a settlement rather than a judgement (with 90% of the cases settling before ever going to trial). Even in just this case of the original article, this person and his family have been traumatized, and he has lost at least a full day of productivity, if not much, much more from the associated fallout.
When a company builds and markets a product that harms people, they should be held liable. Due to the very nature of how machine vision and learning techniques work, they'll never be able to address these problems. And the combination of failure in all four categories makes them particularly destructive.
They should be, however a company building and marketing a harmful product is a separate issue from cops using specious evidence to arrest a man.
Cops (QI aside), are responsible for the actions they take. They shouldn't be able to hide behind "the tools we use are bad", especially when (as a parent poster said), the tool is known to be bad in the first place and the cops still used it.
But literally no one in this thread is arguing to not hold them responsible.
Everyone agrees that yes, the cops and PD are responsible. It's just that some people are arguing that there are other parties that also bear responsibility.
No one thinks the cops should be able to hide behind the fact that the tool is bad. I think these cops should be fired, sued for a wrongful arrest. I think QI should be abolished so wronged party can go after the house of the officer that made the arrest in a civil court. I think the department should be on the hook for a large settlement payment.
But I also think the criminal justice system should enjoin future departments from using this known bad technology. I think we should also be mad at the technology vendors that created this bad tool.
> purchasing officer at the police department
However, if the criminal justice system decides that this is an acceptable use of software, then the criminal justice system itself also bears responsibility.
The developer of the software also bears the responsibility for developing, marketing, and selling the software for the police department.
I agree that the PD bears the majority of the culpability here, but I disagree that it bears every ounce of fault that could exist in this scenario.
The justification for depriving someone of their liberty lies solely with the arresting officer. They can base that on whatever they want, as long as they can later justify it to a court.
For example, you might have a trusted informant who could tell you who committed a local burglary, just this on its own could be legitimate grounds to make an arrest. The same informant might walk into a police station and tell the same information to someone else, for that officer, it might not be sufficient to justify an arrest.
Third, Robert’s arrest demonstrates why claims that face recognition isn’t dangerous are far-removed from reality. Law enforcement has claimed that face recognition technology is only used as an investigative lead and not as the sole basis for arrest. But once the technology falsely identified Robert, there was no real investigation.
I fear this is going to be the norm among police investigations.
The idea behind inclusion is that this product would have never made it to production if the engineering teams, product team, executive team and board members represented the population. But enough representation so that there is a countering voice is even better.
Would have just been "this edge case is not an edge case at all, axe it."
Accurately addressing a market is the point of the corporation more than an illusion of meritocracy amongst the employees.
Afterward, a couple people asked me to put together a list of the examples I cited in my talk. I'll be adding this to my list of examples:
* A hospital AI algorithm discriminating against black people when providing additional healthcare outreach by amplifying racism already in the system. https://www.nature.com/articles/d41586-019-03228-6
* Misdiagnosing people of African decent with genomic variants misclassified as pathogenic due to most of our reference data coming from European/white males. https://www.nejm.org/doi/full/10.1056/NEJMsa1507092
* The dangers of ML in diagnosing Melanoma exacerbating healthcare disparities for darker skinned people. https://jamanetwork.com/journals/jamadermatology/article-abs...
And some other relevant, but not healthcare examples as well:
* When Google's hate speech detecting AI inadvertantly censored anyone who used vernacular referred to in this article as being "African American English". https://fortune.com/2019/08/16/google-jigsaw-perspective-rac...
* When Amazon's AI recruiting tool inadvertantly filtered out resumes from women. https://www.reuters.com/article/us-amazon-com-jobs-automatio...
* When AI criminal risk prediction software used by judges in deciding the severity of punishment for those convicted predicts a higher chance of future offence for a young, black first time offender than for an older white repeat felon. https://www.propublica.org/article/machine-bias-risk-assessm...
And here's some good news though:
* A hospital used AI to enable care and cut costs (though the reporting seems to over simplify and gloss over enough to make the actual analysis of the results a little suspect). https://www.healthcareitnews.com/news/flagler-hospital-uses-...
While there's a lot of head nodding, nothing is ever actually addressed in day to day operations. Data scientists barely know what's going on when they throw things through TensorFlow. What matters is the outcome and the confusion matrix at the end.
I say this as someone who works in data and implements AI/ML platforms. Mr. Williams needs to find the biggest ambulance chasing lawyer and file civil suits not only the law enforcement agencies involved, but top down everyone at DataWorks from the president to the data scientist to the lowly engineer who put this in production.
These people have the power to ruin lives. They need to be made an example of and held accountable for the quality of their work.
>When Amazon's AI recruiting tool inadvertantly filtered out resumes from women
>When Google's hate speech detecting AI inadvertantly censored anyone who used vernacular referred to in this article as being "African American English
There's simply no indication that these aren't statistically valid priors. And we have mountains of scientific evidence to the contrary, but if dared post anything (cited, published literature) I'd be banned. This is all based on the unfounded conflation between equality of outcome and equality of opportunity, and the erasure of evidence of genes and culture playing a role in behavior and life outcomes.
This is bad science.
> the erasure of evidence of genes and culture playing a role in behavior and life outcomes
are concerning.
I'd consider reading the sources I posted in my comment before responding with ill-conceived notions. Literally every single example I posted linked to the peer-reviewed scientific evidence (cited, published literature) indicating the points I summarized.
The only link I posted without peer-reviewed literature was the last one with the positive outcome, and that's the one I commented had suspect analysis.
1. Google's routing algorithm is conditioned on demographics
2. Google's routing algorithm is conditioned on income/wealth
3. Google's routing algorithm is conditioned on crime density
4. Google's routing algorithm cannot condition on anything that would disproportionately route users away from minority neighborhoods
I think the rational choice, to avoid forcing other people to take risks that they may object to, is somewhere between 2 and 3. But the current social zeitgeist seems only to allow for option four, since an optimally sampled dataset will have very strong correlations between 1-3, to the point that in most parts of the us they would all result in the same routing bias.
>The problem was that it mis-classified entire dialects of English (meaning it completely failed at determining sentiment for certain people), deleting all comments from the people of certain cultures
What happens in the case that a particular culture is more hateful? Do we just disregard any data that indicates socially unacceptable bias?
What, only Nazis are capable of hate speech?
That's not what was happening. If you read the link, you'll see the problem is that the AI/ML system was mis-classifying non-hateful speech as hateful, just because of the dialect being used.
If it were the case that the culture was more hateful, then it wouldn't have been considered "mis-classification."
> You're completely missing my point.
I'm not missing your point; it's just not a well-reasoned or substantiated point. Here were your points:
> There's simply no indication that these aren't statistically valid priors.
We do have every indication that this wasn't what was happening in literally every single example I posted. You just have to read them.
> And we have mountains of scientific evidence to the contrary, but if dared post anything (cited, published literature) I'd be banned.
You say that, and yet you keep posting your point without any evidence whatsoever. Meanwhile, every single example I posted did cite peer-reviewed, published scientific evidence.
> This is all based on the unfounded conflation between equality of outcome and equality of opportunity, and the erasure of evidence of genes and culture playing a role in behavior and life outcomes.
Again, peer-reviewed published literature disagrees. Reading it explains why the point that it's all unfounded conflation is incorrect.
The store clerk (who hadn't witnessed the crime and was going off the same frame of video fed into the facial recognition software) said the driver's license photo was a match.
There are several problems with the conduct of the police in this story but IMHO the use of facial recognition is not the most egregious.
"The Shinola shoplifting occurred in October 2018. Katherine Johnston, an investigator at Mackinac Partners, a loss prevention firm, reviewed the store’s surveillance video and sent a copy to the Detroit police"
"In this case, however, according to the Detroit police report, investigators simply included Mr. Williams’s picture in a “6-pack photo lineup” they created and showed to Ms. Johnston, Shinola’s loss-prevention contractor, and she identified him. (Ms. Johnston declined to comment.)"
Our institutions and systems (and maybe humans in general) are not robust enough to cleanly handle these powers, and we are making the same mistake over and over and over again.
The mistake is to treat any police department as a good-faith participant in the goal of reducing police violence. Any tool you give them will be used to brutalize. The only solution is to give them less.
The practice of disclosing one's residence address to the state (for sale to data brokers[1] and accessible by stalkers and the like) when these kinds of abuses are happening is something that needs to stop. There's absolutely no reason that an ID should be gated on the state knowing your residence. It's none of their business. (It's not on a passport. Why is it on a driver's license?)
[1]: https://www.newsweek.com/dmv-drivers-license-data-database-i...
Stop using Amazon Ring and similar doorbell products.
I'm white. I grew up around a sea of white faces. Often when watching a movie filled with a cast of non-white faces, I will have trouble distinguishing one actor from another, especially if they are dressed similarly. This sometimes happens in movies with faces similar to the kinds I grew up surrounded by, but less so.
So unfortunately, yes, I probably do have more trouble distinguishing one black face from another vs one white face from another.
This is known as the cross-race effect and it's only something I became aware of in the last 5-10 years.
Add to that the fallibility of human memory, and I can't believe we still even use line ups. Are there any studies about how often line ups identify the wrong person?
https://en.wikipedia.org/wiki/Cross-race_effect
The shoplifting incident occurred in October 2018 but it wasn’t until March 2019 that the police uploaded the security camera images to the state image-recognition system but the police still waited until the following January to arrest Williams. Unless there was something special about that date in October, there is no way for anyone to remember what they might have been doing on a particular day 15 months previously. Though, as it turns out, the NPR report states that the police did not even try to ascertain whether or not he had an alibi.
Also, after 15 months, there is virtually no chance that any eye-witness (such as the security guard who picked Williams out of a line-up) would be able to recall what the suspect looked like with any degree of certainty or accuracy.
This WUSF article [1] includes a photo of the actual “Investigative Lead Report” and the original image is far too dark for a anyone (human or algorithm) to recognise the person. It’s possible that the original is better quality and better detail can be discerned by applying image-processing filters – but it still looks like a very noisy source.
That same “Investigative Lead Report” also clearly states that “This document is not a positive identification … and is not probable cause to arrest. Further investigation is needed to develop probable cause of arrest”.
The New York Times article [2] states that this facial recognition technology that the Michigan tax-payer has paid millions of dollars for is known to be biased and that the vendors do “not formally measure the systems’ accuracy or bias”.
Finally, the original NPR article states that
> "Most of the time, people who are arrested using face recognition are not told face recognition was used to arrest them," said Jameson Spivack
[1] https://www.wusf.org/the-computer-got-it-wrong-how-facial-re...
[2] https://www.nytimes.com/2020/06/24/technology/facial-recogni...
Many of these cops are earning $200k plus annually! Our law enforcement system is ridiculous and needs an overhaul.
Brookings had a great post about this the other day: https://www.brookings.edu/blog/how-we-rise/2020/06/11/to-add...
Let's say that there are a million people, and the police have photos of 100,000 of them. A crime is committed, and they pull the surveillance of it, and match against their database. They have a funky image matching system that has a false positive rate of 1 in 100,000 people, which is way more accurate than I think facial recognition systems are right now, but let's just roll with it. Of course, on average, this system will produce one positive hit per search. So, the police roll up to that person's home and arrest them.
Then, in court, they get to argue that their system has a 1 in 100,000 false positive rate, so there is a chance of 1 in 100,000 that this person is innocent.
Wrong!
There are ten people in the population of 1 million that the software would comfortably produce a positive hit for. They can't all be the culprit. The chance isn't 1 in 100,000 that the person is innocent - it is in fact at least 9 out of 10 that they are innocent. This person just happens to be the one person out of the ten that would match that had the bad luck to be stored in the police database. Nothing more.
She later won on appeal in part because the defense showed that the testimony and argument of the original statisticians were wrong.
This stuff is so easy to get wrong. A little knowledge of statistics can be dangerous.
This is a meaningless statement, you could choose literally any number for this statement, because you are missing the denominator.
This case of "one in a million" does not happen frequently.
If I'm searching for a murderer in a town of 1000, it takes about 10 independent bits of evidence to get the right one. And when I charge someone, I must already have the vast majority of that evidence. To say "oh well we don't know that it wasn't Mr. or Mrs. Doe, let's bring them in" is itself a breach of the Does' rights. I'm ignoring 9 of the 10 bits of evidence!
Using a low-accuracy facial recognition system and a low-accountability lineup procedure to elevate some random man who did nothing wrong from presumed-innocent to 1-in-6 to prime suspect, without having the necessary amount of evidence, is committing the exact same error and is nearly as egregious as pulling a random civilian out of a hat and charging them.
https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy
Facial recognition technology flagged 26 California lawmakers as criminals. (August 2019)
https://www.mercurynews.com/2019/08/14/facial-recognition-te...