I consider it very important to fight bias in AI, but I fundamentally do not understand this article's take.<p>The thing stopping AI researchers from obtaining unbiased training data is not that we're waiting for laywers to give us permission. The thing stopping us is that the right training data is already hard to find, and unbiased training data is the hardest of all to find because it doesn't exist.<p>Google, for example, does not need permission. They can acquire and train on basically any data they want if they put their mind to it. And Google is <i>completely shit</i> at deploying unbiased AI. (They write great blog posts and presentations about it! Then they don't do it in their actual products.)<p>You won't just find a naturally unbiased dataset. The way to fight AI bias is deliberately and artificially, like in Bolukbasi et al. [1]<p>[1] <a href="https://arxiv.org/abs/1607.06520" rel="nofollow">https://arxiv.org/abs/1607.06520</a>
The term "implicit bias" is a poor choice here.<p>First, it only appears in the title, but not the abstract, for good reason: they are not talking about implicit bias, which as a concept only makes sense when contrasted with explicit bias. Humans can have implicit and explicit bias. An AI can just have "bias" (well, at least until we get true human-level general AI, and we'll be able to talk to it and differentiate what it does from what it says it does).<p>Second, "implicit bias" brings in a lot of unfortunate connotations. It is tied up with the IAT (implicit association test) [1], which is highly controversial. That controversy has no meaning here (again, since there is no explicit bias to contrast it to), it only hurts.<p>Unless I'm missing something, I can only guess that they use the term to grab attention, which is cynical and sad.<p>[1] <a href="https://en.wikipedia.org/wiki/Implicit-association_test#Criticism_and_controversy" rel="nofollow">https://en.wikipedia.org/wiki/Implicit-association_test#Crit...</a>