AI is only able to keep performing as it has learned from the data set. In other words, it keeps the status quo within a few percent of an intended target. Unless given the clear go ahead to just keep learning, at which point they seem to fail at random
I'd say it optimizes something from the point of view of the status quo.
There's nothing in machine learning that bias it into keeping the status quo (differently from people), there's also nothing there biasing it into optimizing the correct thing (also differently from people).
That said, having machines in a consultative position under a judge may be a good thing.
Instead of asking to stop its use, why not ask that it be "supervised" up to the point where its results beat the average judge in a given area of law?
If thinking about setting bail, isn't the target there to keep the bail amounts (or cases where bail is denied) as low as possible while making sure that most people appear in court?
Here a simple metrics would be the average bail and the percentage of people who appeared on court.
On a job like this, I can't really see how a human could beat a machine. And you don't need any kind of "artificial intelligence". This is just standard statistics stuff that every insurance company and bank does.
What does supervision mean in this case? To actually gather data, you'd have to give this system power to actually sentence people. And sentencing people to jail based on the decisions of an explicitly work-in-progress AI purely for the purposes of experimentation is a serious ethical violation.
> These algorithmic outputs inform decisions about bail, sentencing, and parole. Each tool aspires to improve on the accuracy of human decision-making that allows for a better allocation of finite resources.
It's really not clear to me that much is gained from having very precise decisions made about bail and sentencing. Trying to predict the future is a fool's errand, whether a judge does it or a computer. It'd be better to just set fair, uniform standards (particularly for bail where bail should be granted presumptively unless unique circumstances are present).
Unfortunately, using machine learning for sentencing is just the tip of the iceberg. "Scientism" is rife in the criminal justice system. The U.S. Sentencing Guidelines, for example, are utter gibberish. Sentences are calculated to the month using complex formulas: http://www.ussc.gov/guidelines/2016-guidelines-manual/2016-c....
> The total points from subsections (a) through (e) determine the criminal history category in the Sentencing Table in Chapter Five, Part A.
> (a) Add 3 points for each prior sentence of imprisonment exceeding one year and one month.
> (b) Add 2 points for each prior sentence of imprisonment of at least sixty days not counted in (a).
> (c) Add 1 point for each prior sentence not counted in (a) or (b), up to a total of 4 points for this subsection.
> (d) Add 2 points if the defendant committed the instant offense while under any criminal justice sentence, including probation, parole, supervised release, imprisonment, work release, or escape status.
> (e) Add 1 point for each prior sentence resulting from a conviction of a crime of violence that did not receive any points under (a), (b), or (c) above because such sentence was treated as a single sentence, up to a total of 3 points for this subsection.
But it's not like this is based on an empirical statistical model correlating sentences with recidivism or deterrence effects. It's classic scientism, believing that an algorithmic sentence based on completely arbitrary rules is somehow better than an arbitrary sentence handed out by human judgment.
> It's classic scientism, believing that an algorithmic sentence based on completely arbitrary rules is somehow better than an arbitrary sentence handed out by human judgment
it may not be better, but, isn't it more predictable?
also, doesn't it protect a judge and the justice system against charges of favoritism, discrimination, and other kinds of bias?
i mean, i'm not a lawyer and i truly don't know ... but weren't these the sorts of reasons for coming up with these guidelines in the first place?
But the point is that it's fake predictability, as you can't make arbitrarily complex rules and hope to have built the Totally Fair System.
(Provably not, actually.)
The best thing we can do is to admit it's not going to be perfect, and use our own reasoning and feelings, and make sure the process is transparent and the people are held accountable.
so, a criminal defendant and their lawyer have no better chance of predicting the sentence handed down given guidelines than they would if there were no guidelines?
> Guidelines are not a black box that spits out a number of month in prison.
The Sentencing Guidelines are exactly a white box (a black box has concealed internal operations) that spits out sentences.
OTOH, since they aren't mandatory (by Supreme Court ruling; they were originally statutorily mandatory), they don't produce exactly the same rule-based system as they superficially represent.
I don't disagree it could be much better and based on something other than arbitrary nonsense, but these guides are designed to bring consistency to sentences within the same jurisdiction which would make them fairer than completely arbitrary judge sentences. At least everyone is subject to the same arbitrary nonsense guidelines instead of the mood and familiarity of the judge.
I'm reminded of that quote about a "foolish consistency being the hobgoblin of little minds", but when you consider that statistically, people get lighter sentences immediately after a lunch break [1], there's probably some value in removing the human from the equation...
Except that in practice, we are still subject to a judge's mood and familiarity.
I am still trying to clear myself of a wrongful conviction, a misdemeanor for which I was given the maximum allowed sentence of six months in county jail, that was given to me, in spite of blatant inconsistency across half a dozen witness reports and 2 years of court battles, because the judge said, "I think you are lying" and was angry that he had to see me so many times and I didn't just take a plea deal like a good little citizen.
And I wish I was an exception. But this happens everywhere and I doubt the presiding judge could even recount half of these guidelines. It's all arbitrary.
One problem I have seen is that when science is used, people in charge reject the science. One common example is the risk of sex offenders re-offending. It is well known that sex offenders are at a vastly increased risk of offending again. That is also quite false. What data there is on those who re-offend show that sex offenders have some of the lowest rates of recidivism, but people are so conditioned by the incorrect narrative that they reject the science. This includes not just the average person voting for a politician who will make worse laws, but also the people involved in the legal system who decides who gets parole, who gets probation, and how long should sentences last.
Doesn't it depend a little on the what sort of sexual offence?
It seems to me that there is a big range of acts that are classified as sexual offences, starting with a grey area bordering to 'being a dickhead' to some seriously messed up sexuality.
I suppose it's possible to learn how not to be a dickhead, but I also think it's hard to change your sexuality.
> It'd be better to just set fair, uniform standards
Aren't uniform standards as likely as a universal algorithm? There always will be messy edge cases where human judgment is needed. Mandatory sentencing rules worked out badly (though the current U.S. Attorney General Jeff Sessions is still in favor of them!)
I wonder if the algorithm is evidence-based, and learns from the results of prior decisions.
In which case it is possible it would eventually discover that in the USA incarceration is very strongly linked to recidivism. It follows that the algorithm might refuse to incarcerate many convicts.
Which is arguably exactly what the algorithm should do, namely what politicians will/can not: employ evidence to advance the methods and improve the outcomes of the criminal justice system.
The problem with that is that criminality has it's own positive feedback loop.
More crime in an area -> more cops -> more convictions for little stuff -> more big sentences handed out -> more lives ruined -> more crime -> goto start.
Seems like an easier way to break that loop is to decriminalize the "little stuff" instead of capriciously enforcing it. No algorithm is going to be able to do that.
And is that fair? If the last ten guys to come through who looked like me should have had harsher sentences, is it fair that I get that harsher sentence?
> what the algorithm should do, namely what politicians will/can not
It's dangerous to imagine that the algorithms won't be used just as politically as anything else. The difference is, instead of a mostly transparent process in a legislature, the decisions will be made between powerful people and software developers.
As someone who has drafted legislation, I can say with some confidence that if the only difference is the introduction of software programmers, that is positive as they are - on whole - more likely to bear a sense of reason and popular morality than those writing legislation right now.
With the introduction of explicit algorithms we also hopefully will have the advantage of better data collection, which arguably can make it harder to justify changes that operate in contrast to stated principles. (Contrast much legislation that serves purposes entirely orthogonal to their stated intent - and which survive because of the lack of data and accountability that would reveal their abject failures at all but their ulterior motives)
> software programmers, that is positive as they are - on whole - more likely to bear a sense of reason and popular morality than those writing legislation right now
Any time we think some group of people is going to be naturally above human frailties, we are wrong; it never works. We find software programmers to be more reasonable because they are more like us, and also we are imagining ideal hypothetical developers - plenty of real ones should be nowhere near making laws for others.
Everything is ultimately politics. If there is power in algorithms, then those who desire to influence things will influence the algorithms. It's just a less transparent mechanism.
The supposition that software programmers are more ethical than the wealthy is based on the observation Paul Piffs coined as the "Asshole Effect".
You will note my choice of words "on whole", which essentially means a statistical aggregation.
But you are absolutely right that everything is politics. I'm not sure I agree about the transparency. Point of interest: You probably know more about the technical exploits the CIA and NSA had than the legal and political mechanisms that permitted them to come about - and almost certainly never will.
based on some studies I've seen, human judges are terrible due to basic human nature, so I'd like to see something happen to make things more objective. Of course, the algo would have to be open, as opposed to the proprietary software being used now.
For example just having your sentencing be before lunch or at the end of the day results in harsher punishment simply due to the judge being hungry or tired.
Same goes for other basic things like women getting lighter sentences, minorities harsher sentences, etc.
Apparently washing your hands also causes you to be more lenient.
So AI probably shouldn't be used for sentencing, but that's more to do with all the stakeholders being clueless about the technology. The people asking for it, paying for it, using it, and building it (hey I can just throw some packages together in R right?) don't know what they're doing and are using it to significantly impact the lives of others.
On the other hand, pretending an arbitrary Judge / Jury / sentencing guidelines also don't form a dynamic system with not well understood effects that's equivalent to random AI in terms of output doesn't exactly help anyone either.
At the end of the day you need people who are honorable who you can trust to do the right thing (as much as it sounds like a saturday morning cartoon).
> Same goes for other basic things like women getting lighter sentences, minorities harsher sentences, etc.
The problem is, "AI" today is glorified pattern matching. So, it learns what "should be" based on the current state. In other words because the pattern exists it will learn precisely that "women should get lighter sentences and minorities should get harsher ones"
Not only that, they are so good at pattern matching that you don't even need to provide the gender or race for them to identify those as a parameter.
It would be great if we could train an AI to eliminate bias, but as it is they are training to reinforce bias that already exists.
Until we get natural language understanding and context aware AI, using AI for sentencing is a terrible idea.
One AI extracts faces of the suspect from camera footage
Another AI sifts through meta data to collect evidence about suspect
A third AI assists in building the case for the prosector, digging through hundreds of years of documented precedence to establish positions.
Now a fourth AI is assisting in sentencing... and all of a sudden, heels are getting dug in on some "How could this have happened!? We must stop it!" nonsense.
The AI justice cow left the barn a long time ago, folks.
EDIT: I authorize anyone to engage in creative derivations of "AI Justice Cow" free of charge, license, restraint, and responsibility.
>While this can be the fastest route, the GPS’s algorithm does not concern itself with factors important to truckers carrying a heavy load, such as the 43’s 1,300-foot elevation drop over four miles with two sharp turns.
I know this is somewhat off topic but the lack of advanced options for GPS routing is such a PITA. It would be trivial to add check-boxes for things like:
"I'm towing a trailer, don't make me take dumb lefts across multiple lanes, avoid clustterfawks and don't make me take unnecessary turns"
"Yes I'm wealthy enough to afford an iphone, that doesn't mean I want you to send me through a $5 bridge toll"
"I'm taking a road-trip, send me on a route that uses ten fewer roads even if it takes twenty more minutes, I don't want to have to look for a turn every 10min"
I know tons of options aren't good for the UI but just hiding all that stuff behind an "advanced preferences" menu or something would be nice.
Just a simple tie in to a weather API that increases the cost of route features that are a PITA in snow would be nice (no I don't want to stop on a downhill to take a >90deg left across 40mph traffic in snow thank you very much).
But I agree with the other ones. Quite a few of them might need better recognition of weight restrictions and other such rules on roadways, though. It in theory shouldn't be hard to develop (self-driving tech already either reads signs or sources data from previous mappings of roads where said signs have been read), but I wouldn't be surprised if this info hasn't yet been catalogued yet.
As far as weight restrictions and stuff, I'm talking even simpler than that. The geospatial data and a lot of the traffic flow is already there. It would be an "summer intern size" project to figure out an algorithm that does an ok job identifying route features and traffic flow situations that are not trailer friendly or delivery vehicle friendly (left turns suck). Even for something other than commercial vehicles this is important. Anyone driving a mini-bus with a student/church/summer camp group will run into a route with a really bad intersection or turn.
Have you tried installing an actual GPS navigation app (as opposed to just Google Maps)? At least the last time I used one (I think it was CoPilot a few years back) you could specify stuff like "avoid toll roads", "prefer simplest route", and set the size of your vehicle all in the settings menu.
I don't use my phone for my primary nav device, I use a decade old Garmin for most stuff but if I'm going somewhere I've never been on a more than 1min notice I just Google map it and plan my own route based on that. In urban areas street view is available and I can use that to make sure the Google route and my revisions are sane. In rural areas there aren't as many options or pitfalls so GPS generally works well if. Both the decade old GPS and Google maps have about the same effectiveness when it comes to finding residential addresses on small streets.
These would be good options, but to make it intuitive and not overly complex you probably need to group it into a few major use-cases. I recently rented a car and the built-in nav offered 4 route options that I'm pretty sure (tough to interpret some of the icons) broke down into: optimize for time, optimize for fuel, optimize for distance (don't know when I would choose this one - I would think trying to minimize wear on the car would be better when optimizing for fuel), optimize for turns. I think that last one could be improved for the towing use case you mentioned - I would often trade several minutes to make 3 right turns to avoid a left, but for the most part I'd also just like to get to some road and then not worry about any lane changes for a while. Maybe "fewest turns" and "fewest left turns" should be separate options. There's also a "no tolls" option on every nav system I've used, but I wish you could configure the trade-off there too- there's a dollar amount I'm willing to pay for every 10 minutes it saves me, depending on the day.
Special GPSes for truckers are available from companies like TomTom and Garmin.
If truckers are choosing to save a few hundred bucks by using their smartphones, which don't have the trucker-specific features, surely the fault lies with the trucker for using a consumer-grade product, not with the consumer-grade product for not being professional-grade?
Weight limits, clearances, weight stations, truck routes, city boundaries (transporting material restricted in a city?)...
There is a whole host of features that are trucker specific. Trying to reconcile the hard copy guide for the possible route and a phone based route planner is something I don't want to imagine.
A truck GPS is extremely helpful if you're driving a commercial vehicle any appreciable distance in an unfamiliar place. They include many additional data points in routing: vehicle height for overpass clearance, vehicle weight for bridges, "No Trucks"/"Passenger Cars Only"/"No Commercial Vehicles" restrictions etc. You can get yourself into hot water if you're following car GPS directions with even a mid-sized U-Haul. See: http://11foot8.com
You're already in hot water with a U-Haul. Anecotally, they have been the worst cargo vehicle rental business in my life. Get a truck from Budget, Hertz, or Penske instead. Every friend whom I have helped with their move has experienced a mechanical failure of some kind with a U-Haul vehicle that significantly increased the stress of an already stressful event.
Know your clearance and pay attention to road signs. Your regular car insurance may cover rental cars, but it probably doesn't cover rental trucks. So if you peel your top on a low underpass, you may find yourself in a world of pain and regret for a long time. Ideally, if you are in a rental truck, you should already know how to get where you are going without GPS assistance.
Indeed, I used U-Haul as kind of generic word for rental truck here, but I much prefer Penske if I'm renting a truck. They have fewer locations but their trucks are clearly better maintained.
Or, to cite another case that I see quite often on my commute home: I'm driving a tall panel truck, don't route me onto routes with low-clearance overpasses...
I see beer trucks and U-Hauls with the top six to twelve inches sheared off by the railroad overpass at least once a month.
Google maps normally gives you a few routes and you can choose the best. You can avoid highways, tolls, and ferries. You can preview all your turns. You can add stops if you want. I don't believe Google maps has many weather suggestions, but there are apps to check the whether.
I've found Google maps to be better than any built-in GPS in my cars.
Playing devils advocate I'd say that the main problem with employing AI would be that it will how bad decisions humans make. This is exactly the kind of case where computers, just relying on hard data would do much better than humans.
The article complains that AI is a black box for the defendant. How is this any different from judge's brain? You can't peek into his mind to figure out what is behind the decision. Judge can give some justifications, but you won't know if those are the real reasons or if the decision is just mostly based on defendants skin color, socioeconomic background or clothing.
One of my big concerns is whether the algorithms are institutionalizing racism, with legal decisions that make it impossible to challenge these.
After all, the algorithm has been trained on information about recidivism that was collected in a world where racism skews arrest rates, conviction rates, and sentencing.[1] That means the algorithm is almost certainly baking in a racial bias. Now, I'm sure they aren't foolish enough to put "race" in as one of the input factors, but other correlated factors will allow the algorithm to continue to enforce this racism, but now with legal immunity.
Some proxies for race:
- Where somebody lives
- Their name
- What sports teams somebody supports
- Their finances
- consumer preferences
- education level
- what medical conditions somebody has
- political leanings
I'm not saying these factors can't legitimately be used in a risk assessment, but they could be used to make a good bet on race.
I don't see how those factors are relevant at all, regardless of the race of the criminal. The problem to address is the lack of transparency. Trying to guess what inputs are being factored in is pointless.
Finances? While it is true that blacks and native americans have the highest poverty rates, if you take a random poor person they are about 50% more likely to be white than black. Same with the many of your other "proxies". Therefore they would be a poor bet for race.
If you were using these proxies to identify the race of a person jailed for a crime, they might be good predictors, but only marginally more useful than just blindly guessing black, since they constitute the majority of incarcerated people.
I think, by attempting to remove the smoke screens a "real racist" would use, you give them a new one - "look, our opponents want to ignore actual data".
Even if you are careful to remove all features you think could tell the race (let's say geographic location etc), it could still be giving harsher sentences to minorities if those are treated differently upfront. For instance, it would make sense for the model to learn that the more charges count the harsher the sentence. This sounds right: a bank robbery should be less punished than a bank robbery + a carjacking, at least in the US judicial system.
But now let's say minorities gets a "resiting arrest" charge on top of their original charge more often, because of many factors such as the police bias, the bias of the minorities towards police, etc. (By bias here I mean all spectrum : racism, but also fear of the police etc).
As long as minorities are treated differently upfront, then the model would treat them differently.
But if all of that is true, the model doesn't change the equation. The problems you outline need to be addressed upstream. Trying to account for and correct them at the point of sentencing is very problematic.
"It found that widely used software that assessed the risk of recidivism in criminals was twice as likely to mistakenly flag black defendants as being at a higher risk of committing future crimes. It was also twice as likely to incorrectly flag white defendants as low risk."
> ...other correlated factors will allow the algorithm to continue to enforce this racism, but now with legal immunity.
I'm sympathetic, but how do we keep from causing more harm than good here? It seems like blindly ignoring all data that correlates with race is also bad. We do this in some circumstances already (i.e., IQ tests when doing hiring or admissions) and I think it's a defensible point that we're worse off for it.
> blindly ignoring all data that correlates with race is also bad
This is a very theoretical, futuristic red herring. Race factors into decision-making far, far too much. It's like worrying that if someone developed a perfect, bug-free crypto library, then everyone would use it and we'd have a monoculture - not really a present concern, and in fact a good problem to have (nor is it an eloquent analogy, but I have to run).
Yeah, there's a good book on this very issue called "Weapons of Math Destruction."[1] The author did an interview on a podcast and discusses how algorithms used by these judges reflect our racial biases.[2] One example that I remember is that the algorithm takes into consideration one's zip code, which correlates strongly with race in the US.
I agree wholeheartedly with this concern, but I think there is also a counter-argument too. If you assume we're starting from a situation where there is bias, then you probably want to look for ways to reduce that bias. The software may have an advantage here too.
It may be easier to objectively measure and correct for undesired (racial, or otherwise) bias in an automated system than it is to objectively measure and correct for bias in human judgement. And even if it's more difficult to correct for undesired bias in an automated system, at least that correction is more likely to be permanent and applied uniformly, compared to things like giving training courses for humans, which will vary in effectiveness across the recipients of the course, and will need to be refreshed frequently.
There are a lot of qualifications and "maybe"s in that argument though.
There was a Washington Post article and arXiv paper on this topic several months ago. [1][2] The problem boils down to the definition of bias. Northpointe, the company that owns COMPAS, says the algorithm treats black and white defendants equally, which is true. Others claim that its predictions for blacks are wrong more often than for whites, which is also true. The main point of the paper is that you can't have your cake and eat it too. If you don't want to consider race, then the predictions will be wrong more often for blacks. If you don't want the predictions to be wrong more often for blacks, the algorithm has to consider race. (I'd recommend the articles for a better explanation.)
In my mind, the ownership of the software is what's troubling. First, it's not public, so nobody can look at the guts. And second, the Loomis decision constructs even more walls around the software. It's hard for people to critically examine and change something they can't see.
On the other hand you could probably find ways to make the algorithms less racist than their human equivalent. No system is totally unbiased but doing better than what we have is possible.
Chris Stucchio wrote an interesting blog post which changed my mind on this. Instead of exacerbating it, a Machine Learning model can actively correct for bias.
Bias correction is possible, but, tautologically, a model whose structure cannot capture a bias cannot correct for that bias. This means that a modeller must either understand the bias, and accommodate for it in their model, or use a model that might be able to capture unknown structures and run the (serious) risk of over-fitting that model.
Using traditional machine learning techniques for this purpose is a non-starter and completely unacceptable. Neural networks are just a black box, and don't produce an inspectable justification or reasoning. The best you can say is that the model correctly predicts recidivism in X% of cases for some sample.
I'm not opposed to using other algorithmic methods, but the algorithm needs to be transparent. Though that would be difficult to do outside of some pretty tightly controlled parameters. We can't currently make a system that can take into account arbitrary facts about the case and weigh ethical implications.
Could also lead to a type of self fulfilling prophesy, by ignoring the 'individual' in the decision making process, thusly groups being targeted will learn over time that their personal efforts to reform are a waste of time.
Even if the software was statistically perfect and somehow immune to our cultural biases, it should still not be used. An individual stands before the judge, not a statistical demographic group. The probability of an imaginary statistical individual's recidivism is irrelevant; what is relevant is the current state of that individual. Relying on statistical models is both lazy and unfair
I wonder if it were possible to get a copy of the software so as to figure out what the best possible responses to a presentencing interview would be in order to get the most favorable computation.
Like - Yes, I'm very social. I play bridge, take my kids to soccer, I'm a member of the PTA. Yes, I exercise. I have a weight set at home, ride my bicycle, play racquetball at the gym. No sir, I don't do drugs, never touched them. No sir, I don't drink either. Yes sir, I do have a degree, two in fact!
Just in case you might need it at a presentencing interview, of course.
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89 comments
[ 3.2 ms ] story [ 169 ms ] threadThere's nothing in machine learning that bias it into keeping the status quo (differently from people), there's also nothing there biasing it into optimizing the correct thing (also differently from people).
That said, having machines in a consultative position under a judge may be a good thing.
Why should it stop when it has the potential to be much more efficient? Just give it sufficient oversight, and I'm fine with this.
Here a simple metrics would be the average bail and the percentage of people who appeared on court.
On a job like this, I can't really see how a human could beat a machine. And you don't need any kind of "artificial intelligence". This is just standard statistics stuff that every insurance company and bank does.
See http://blogs.discovermagazine.com/notrocketscience/2011/04/1... for the example of the problem we need to solve.
Could we demand that they pass a course in machine learning before they use it?
It's really not clear to me that much is gained from having very precise decisions made about bail and sentencing. Trying to predict the future is a fool's errand, whether a judge does it or a computer. It'd be better to just set fair, uniform standards (particularly for bail where bail should be granted presumptively unless unique circumstances are present).
Unfortunately, using machine learning for sentencing is just the tip of the iceberg. "Scientism" is rife in the criminal justice system. The U.S. Sentencing Guidelines, for example, are utter gibberish. Sentences are calculated to the month using complex formulas: http://www.ussc.gov/guidelines/2016-guidelines-manual/2016-c....
> The total points from subsections (a) through (e) determine the criminal history category in the Sentencing Table in Chapter Five, Part A.
> (a) Add 3 points for each prior sentence of imprisonment exceeding one year and one month.
> (b) Add 2 points for each prior sentence of imprisonment of at least sixty days not counted in (a).
> (c) Add 1 point for each prior sentence not counted in (a) or (b), up to a total of 4 points for this subsection.
> (d) Add 2 points if the defendant committed the instant offense while under any criminal justice sentence, including probation, parole, supervised release, imprisonment, work release, or escape status.
> (e) Add 1 point for each prior sentence resulting from a conviction of a crime of violence that did not receive any points under (a), (b), or (c) above because such sentence was treated as a single sentence, up to a total of 3 points for this subsection.
But it's not like this is based on an empirical statistical model correlating sentences with recidivism or deterrence effects. It's classic scientism, believing that an algorithmic sentence based on completely arbitrary rules is somehow better than an arbitrary sentence handed out by human judgment.
it may not be better, but, isn't it more predictable?
also, doesn't it protect a judge and the justice system against charges of favoritism, discrimination, and other kinds of bias?
i mean, i'm not a lawyer and i truly don't know ... but weren't these the sorts of reasons for coming up with these guidelines in the first place?
(Provably not, actually.)
The best thing we can do is to admit it's not going to be perfect, and use our own reasoning and feelings, and make sure the process is transparent and the people are held accountable.
so, a criminal defendant and their lawyer have no better chance of predicting the sentence handed down given guidelines than they would if there were no guidelines?
Guidelines are not a black box that spits out a number of month in prison.
The Sentencing Guidelines are exactly a white box (a black box has concealed internal operations) that spits out sentences.
OTOH, since they aren't mandatory (by Supreme Court ruling; they were originally statutorily mandatory), they don't produce exactly the same rule-based system as they superficially represent.
[1]: http://www.pnas.org/content/108/17/6889
I am still trying to clear myself of a wrongful conviction, a misdemeanor for which I was given the maximum allowed sentence of six months in county jail, that was given to me, in spite of blatant inconsistency across half a dozen witness reports and 2 years of court battles, because the judge said, "I think you are lying" and was angry that he had to see me so many times and I didn't just take a plea deal like a good little citizen.
And I wish I was an exception. But this happens everywhere and I doubt the presiding judge could even recount half of these guidelines. It's all arbitrary.
Perhaps the real objection is the lack of transparency behind the calculations?
It seems to me that there is a big range of acts that are classified as sexual offences, starting with a grey area bordering to 'being a dickhead' to some seriously messed up sexuality.
I suppose it's possible to learn how not to be a dickhead, but I also think it's hard to change your sexuality.
Aren't uniform standards as likely as a universal algorithm? There always will be messy edge cases where human judgment is needed. Mandatory sentencing rules worked out badly (though the current U.S. Attorney General Jeff Sessions is still in favor of them!)
Or am I misunderstanding you?
"Numerology" might be a better word.
In which case it is possible it would eventually discover that in the USA incarceration is very strongly linked to recidivism. It follows that the algorithm might refuse to incarcerate many convicts.
Which is arguably exactly what the algorithm should do, namely what politicians will/can not: employ evidence to advance the methods and improve the outcomes of the criminal justice system.
More crime in an area -> more cops -> more convictions for little stuff -> more big sentences handed out -> more lives ruined -> more crime -> goto start.
Seems like an easier way to break that loop is to decriminalize the "little stuff" instead of capriciously enforcing it. No algorithm is going to be able to do that.
And is that fair? If the last ten guys to come through who looked like me should have had harsher sentences, is it fair that I get that harsher sentence?
It's dangerous to imagine that the algorithms won't be used just as politically as anything else. The difference is, instead of a mostly transparent process in a legislature, the decisions will be made between powerful people and software developers.
With the introduction of explicit algorithms we also hopefully will have the advantage of better data collection, which arguably can make it harder to justify changes that operate in contrast to stated principles. (Contrast much legislation that serves purposes entirely orthogonal to their stated intent - and which survive because of the lack of data and accountability that would reveal their abject failures at all but their ulterior motives)
Any time we think some group of people is going to be naturally above human frailties, we are wrong; it never works. We find software programmers to be more reasonable because they are more like us, and also we are imagining ideal hypothetical developers - plenty of real ones should be nowhere near making laws for others.
Everything is ultimately politics. If there is power in algorithms, then those who desire to influence things will influence the algorithms. It's just a less transparent mechanism.
You will note my choice of words "on whole", which essentially means a statistical aggregation.
But you are absolutely right that everything is politics. I'm not sure I agree about the transparency. Point of interest: You probably know more about the technical exploits the CIA and NSA had than the legal and political mechanisms that permitted them to come about - and almost certainly never will.
For example just having your sentencing be before lunch or at the end of the day results in harsher punishment simply due to the judge being hungry or tired.
Same goes for other basic things like women getting lighter sentences, minorities harsher sentences, etc.
So AI probably shouldn't be used for sentencing, but that's more to do with all the stakeholders being clueless about the technology. The people asking for it, paying for it, using it, and building it (hey I can just throw some packages together in R right?) don't know what they're doing and are using it to significantly impact the lives of others.
On the other hand, pretending an arbitrary Judge / Jury / sentencing guidelines also don't form a dynamic system with not well understood effects that's equivalent to random AI in terms of output doesn't exactly help anyone either.
At the end of the day you need people who are honorable who you can trust to do the right thing (as much as it sounds like a saturday morning cartoon).
The problem is, "AI" today is glorified pattern matching. So, it learns what "should be" based on the current state. In other words because the pattern exists it will learn precisely that "women should get lighter sentences and minorities should get harsher ones"
Not only that, they are so good at pattern matching that you don't even need to provide the gender or race for them to identify those as a parameter.
It would be great if we could train an AI to eliminate bias, but as it is they are training to reinforce bias that already exists.
Until we get natural language understanding and context aware AI, using AI for sentencing is a terrible idea.
(There's an analogy to vehicle automation, where we accept a level of error in humans that we wouldn't in automation.)
Another AI sifts through meta data to collect evidence about suspect
A third AI assists in building the case for the prosector, digging through hundreds of years of documented precedence to establish positions.
Now a fourth AI is assisting in sentencing... and all of a sudden, heels are getting dug in on some "How could this have happened!? We must stop it!" nonsense.
The AI justice cow left the barn a long time ago, folks.
EDIT: I authorize anyone to engage in creative derivations of "AI Justice Cow" free of charge, license, restraint, and responsibility.
I know this is somewhat off topic but the lack of advanced options for GPS routing is such a PITA. It would be trivial to add check-boxes for things like:
"I'm towing a trailer, don't make me take dumb lefts across multiple lanes, avoid clustterfawks and don't make me take unnecessary turns"
"Yes I'm wealthy enough to afford an iphone, that doesn't mean I want you to send me through a $5 bridge toll"
"I'm taking a road-trip, send me on a route that uses ten fewer roads even if it takes twenty more minutes, I don't want to have to look for a turn every 10min"
I know tons of options aren't good for the UI but just hiding all that stuff behind an "advanced preferences" menu or something would be nice.
Just a simple tie in to a weather API that increases the cost of route features that are a PITA in snow would be nice (no I don't want to stop on a downhill to take a >90deg left across 40mph traffic in snow thank you very much).
https://www.google.com/search?q=apple+maps+avoid+tolls
https://www.google.com/search?q=google+maps+avoid+tolls
But I agree with the other ones. Quite a few of them might need better recognition of weight restrictions and other such rules on roadways, though. It in theory shouldn't be hard to develop (self-driving tech already either reads signs or sources data from previous mappings of roads where said signs have been read), but I wouldn't be surprised if this info hasn't yet been catalogued yet.
As far as weight restrictions and stuff, I'm talking even simpler than that. The geospatial data and a lot of the traffic flow is already there. It would be an "summer intern size" project to figure out an algorithm that does an ok job identifying route features and traffic flow situations that are not trailer friendly or delivery vehicle friendly (left turns suck). Even for something other than commercial vehicles this is important. Anyone driving a mini-bus with a student/church/summer camp group will run into a route with a really bad intersection or turn.
If truckers are choosing to save a few hundred bucks by using their smartphones, which don't have the trucker-specific features, surely the fault lies with the trucker for using a consumer-grade product, not with the consumer-grade product for not being professional-grade?
There is a whole host of features that are trucker specific. Trying to reconcile the hard copy guide for the possible route and a phone based route planner is something I don't want to imagine.
Know your clearance and pay attention to road signs. Your regular car insurance may cover rental cars, but it probably doesn't cover rental trucks. So if you peel your top on a low underpass, you may find yourself in a world of pain and regret for a long time. Ideally, if you are in a rental truck, you should already know how to get where you are going without GPS assistance.
I see beer trucks and U-Hauls with the top six to twelve inches sheared off by the railroad overpass at least once a month.
The article complains that AI is a black box for the defendant. How is this any different from judge's brain? You can't peek into his mind to figure out what is behind the decision. Judge can give some justifications, but you won't know if those are the real reasons or if the decision is just mostly based on defendants skin color, socioeconomic background or clothing.
Humans can acknowledge and correct for biases in training data much better than computers can.
EDIT: this person put it much better than I have: https://news.ycombinator.com/item?id=14139772
After all, the algorithm has been trained on information about recidivism that was collected in a world where racism skews arrest rates, conviction rates, and sentencing.[1] That means the algorithm is almost certainly baking in a racial bias. Now, I'm sure they aren't foolish enough to put "race" in as one of the input factors, but other correlated factors will allow the algorithm to continue to enforce this racism, but now with legal immunity.
[1] Do I really need to footnote this? http://www.huffingtonpost.com/kim-farbota/black-crime-rates-... is one source that addresses all of these, but there are many, many other sources.
Which correlated factors are you thinking of?
I'm not saying these factors can't legitimately be used in a risk assessment, but they could be used to make a good bet on race.
If you were using these proxies to identify the race of a person jailed for a crime, they might be good predictors, but only marginally more useful than just blindly guessing black, since they constitute the majority of incarcerated people.
I think, by attempting to remove the smoke screens a "real racist" would use, you give them a new one - "look, our opponents want to ignore actual data".
But now let's say minorities gets a "resiting arrest" charge on top of their original charge more often, because of many factors such as the police bias, the bias of the minorities towards police, etc. (By bias here I mean all spectrum : racism, but also fear of the police etc).
As long as minorities are treated differently upfront, then the model would treat them differently.
"It found that widely used software that assessed the risk of recidivism in criminals was twice as likely to mistakenly flag black defendants as being at a higher risk of committing future crimes. It was also twice as likely to incorrectly flag white defendants as low risk."
Source: https://www.nytimes.com/2016/06/26/opinion/sunday/artificial...
I'm sympathetic, but how do we keep from causing more harm than good here? It seems like blindly ignoring all data that correlates with race is also bad. We do this in some circumstances already (i.e., IQ tests when doing hiring or admissions) and I think it's a defensible point that we're worse off for it.
This is a very theoretical, futuristic red herring. Race factors into decision-making far, far too much. It's like worrying that if someone developed a perfect, bug-free crypto library, then everyone would use it and we'd have a monoculture - not really a present concern, and in fact a good problem to have (nor is it an eloquent analogy, but I have to run).
I think there are fair existing examples both for and against it, so I'm not sure calling either position futuristic or theoretical is fair.
[1]: https://weaponsofmathdestructionbook.com/ [2]: http://www.econtalk.org/archives/2016/10/cathy_oneil_on_1.ht...
It may be easier to objectively measure and correct for undesired (racial, or otherwise) bias in an automated system than it is to objectively measure and correct for bias in human judgement. And even if it's more difficult to correct for undesired bias in an automated system, at least that correction is more likely to be permanent and applied uniformly, compared to things like giving training courses for humans, which will vary in effectiveness across the recipients of the course, and will need to be refreshed frequently.
There are a lot of qualifications and "maybe"s in that argument though.
In my mind, the ownership of the software is what's troubling. First, it's not public, so nobody can look at the guts. And second, the Loomis decision constructs even more walls around the software. It's hard for people to critically examine and change something they can't see.
[1] https://www.washingtonpost.com/news/monkey-cage/wp/2016/10/1... [2] https://arxiv.org/pdf/1609.05807.pdf
https://www.chrisstucchio.com/blog/2016/alien_intelligences_...
Bias correction is possible, but, tautologically, a model whose structure cannot capture a bias cannot correct for that bias. This means that a modeller must either understand the bias, and accommodate for it in their model, or use a model that might be able to capture unknown structures and run the (serious) risk of over-fitting that model.
I'm not opposed to using other algorithmic methods, but the algorithm needs to be transparent. Though that would be difficult to do outside of some pretty tightly controlled parameters. We can't currently make a system that can take into account arbitrary facts about the case and weigh ethical implications.
I hope this mentality doesn't make it up to the federal Supreme Court. This is definitely a faulty argument presented by the state to be sure.
Like - Yes, I'm very social. I play bridge, take my kids to soccer, I'm a member of the PTA. Yes, I exercise. I have a weight set at home, ride my bicycle, play racquetball at the gym. No sir, I don't do drugs, never touched them. No sir, I don't drink either. Yes sir, I do have a degree, two in fact!
Just in case you might need it at a presentencing interview, of course.