In recent years, shipping boats produce 3.1% of global yearly CO2 emissions, more than the entire country of Germany. In order to minimize their internal costs, most of the container shipping companies use very low grade fuel in enormous quantities, which leads to increased amounts of sulphur in the air, among other toxic substances. It has been estimated that one container ship can emit as much pollution as 50 million cars, and 60,000 deaths worldwide are attributed indirectly to cargo ship industry pollution related issues annually. Even industry-friendly sources like the World Shipping Council admit that thousands of containers are lost each year, on the ocean floor or drifting loose. holy crap...
There's a lot of truth in this essay but there are also a lot of unsubstantiated claims made by two people who clearly don't actually know how the Echo and Alexa are developed. The tone and language implies they know what they're talking about and express everything as fact even though they're completely wrong about some of it. The sensationalism and vilifying really detract from an otherwise important topic that should be discussed more: the environmental impact of modern technology and how sustainable it is.
So Crawford doesn't actually ever assert that MTurk is used directly for Alexa, only that it's one common form of commoditized data labor, of which the internal teams of annotators is actually another good example.
The language thing is even more interesting. If you read Crawford's research, her whole thing is about how machine learning is full of bias, for example about how AmazonFresh availability maps resemble 1930s segregation maps. Yes, it gets kind of philosophical on the discussion, and I'm nowhere near qualified enough to get into a discussion about this, but the thing that I'm curious is, how much does the data reflect the biases of those language engineers?
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The language thing is even more interesting. If you read Crawford's research, her whole thing is about how machine learning is full of bias, for example about how AmazonFresh availability maps resemble 1930s segregation maps. Yes, it gets kind of philosophical on the discussion, and I'm nowhere near qualified enough to get into a discussion about this, but the thing that I'm curious is, how much does the data reflect the biases of those language engineers?