> The problem with them using words like 'fairness' is anyone who disagrees with what they're doing is immediately put on the defensive and labelled as 'unfair'

> The problem with them using words like 'fairness' is anyone who disagrees with what they're doing is immediately put on the defensive and labelled as 'unfair'. This is far from the truth though, as what society today is actually doing, is trying to figure out exactly what 'fair' should mean. Google et al just took it upon themselves to start pushing their particular brand of fairness onto society and acting as if it's the most natural widely-accepted common sense. I'm sorry, but this comment seems reactionary in the extreme. Could you identify aspects of the courseware itself that are "unfair" in the sense you're using? Or give a misleading definition of "fair"? > Not enough <demographic> represented in the workforce? Bias the candidate selection model to equalize it. Some <demographic> is over-represented in crime statistics? Bias jail sentencing model to be more lenient on them and hope it equalizes their representation in stats over time. Further, followups like this suggest you're afraid that this is a play by Google to lead to the (presumably undesirable, from your tone) outcome of fairness. You're not-very-subtly suggesting it'd be unfair for black people to do less jail time for the same crimes, which has no relevance to the conversation at hand. Why include this? Is this a subtle signal? A tangent slide you fell down? A bad edit of a post? > I don't know what 'fairness' should actually mean in modern society, but we definitely need to be aware that the US tech industry is hijacking the term to fit their agenda, and need to be checked. And the exact nature of the biases they're injecting should definitely be made transparent and put to public debate. It's also extremely important to note that cultural elements are using the same processes to normalize acute unfairness in the workplace and in policy, often under the cover of "the machine predicted it." Making sure that a broader class of people know that all machine learning does is fit models to pre-existing data (and therefore are quite limited in what kind of novel insight they can glean safely, requiring utmost care in the model design and data normalization) is an incredibly good idea, both for the industry itself and for society as a whole. It's in the nearly the exact same vein as, "I wish more people understood the implications of Bayes/Laplace's rule or the law of large numbers." The only downside is that fewer folks will be able to peddle complex-sounding ML methods that boil down to (from a practitioner's standpoint) a few lines of trivial Python as if they are the cutting edge of AI research.