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Bias in AI (whatever the source or nature of the problem) is a real issue that needs addressing, but taking the relevant training data off of ImageNet seems lik
by maxander 7y ago
Bias in AI (whatever the source or nature of the problem) is a real issue that needs addressing, but taking the relevant training data off of ImageNet seems like a perfect example of papering over a problem to avoid really confronting it. We will need to find ways to make AI programs than can see beyond the biases (about humans or otherwise) that will always exist to some degree in real-world data.
If the ImageNet contains bias that leads to embarrassing results- that’s fine! That give us a readily available toy instance of the problem to study. Taking that away could actively harm anti-bias research.
- avinium 7y agoI strongly prefer they keep the bias. It shows plainly that these models have zero intelligence, they're just pattern matching over specific datasets. I don't want the appearance of fairness (introduced by human dataset curation) to be mistaken for "intelligence". Keeping the bias would hopefully cause people to think more critically about why such bias exists in the model in the first place.
- jobigoud 7y agoDisagree that it proves zero intelligence. Intelligent humans also have bias.
- avinium 7y agoI'm not saying that intelligence and bias are orthogonal. That discussion requires a much deeper consideration of human cognition and psychology :) I'm just saying that model bias is a very easy thing to explain (usually, data imbalance). You can also fiddle with the label ratios to change the bias - which is also a good way of showing that the models aren't really intelligent.
- WilliamEdward 7y agoThey could store it somewhere non-public for research purposes, but taking it off the website was the right call.
- raven105x 7y agoBias: prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. If a data set is flawed, it should be fixed, but when ML finds objective patterns our culture finds subjectively unpalatable and we choose to "fix" them, we fall prey to and re-enact the same grade of self-delusion exhibited by, for example, "the church" in the dark ages. Computer science is already low in terms of accountability & rigor compared to other fields without these kinds of suggestions.
- krainboltgreene 7y ago> "the church" in the dark ages This isn't a thing and you should read more about what you think happened in this period of western history.
- raven105x 7y agoI said nothing of Western history and am familiar with the subject. Also, this is completely unrelated to my point so you will receive no further responses on it.
- krainboltgreene 7y agoIt's the foundation of your point.
- jeegsy 7y agoIf we say the dataset is flawed, it is important that we pin down why and how exactly it is flawed. By what methods exactly are we going to source "not-flawed" data?
- crooked-v 7y agoYou use the phrase 'objective pattern' here, but that conflates causation and correlation. For example: black men in the US are more likely than white men to have criminal records, but this in no way means that black men are "objectively" more criminal.