AI Explorables: big ideas in machine learning, simply explained
Nobody is actually smarter or better than anyone at anything. It&#x27;s a result of biases and privilege. And if all biases were taken into consideration, everyone would (and should) score exactly the same on everything, regardless of age, sex, race, gender, ethnicity, orientation, etc etc.<p>That&#x27;s the underlying message about ML initiatives that touch sensitive topics. Yes, we should be very careful not to encode biases into our models, and these biases are definitely real. Absolutely 100%! But what happens <i>if</i> the models still show disparity? Are we prepared for that possibility? Does it mean there are more biases yet to uncover and we must root them out? Or should we hide anything that reveals disparity in the absence of biases? Or are all biases impossible to remove, so they must forever remain the explanation for all disparity? Is disparity a proof of bias? Because if that is true, then the only conclusion is that every person is exactly functionally equal and the only differences are human biases.<p>I&#x27;ve seen smart tech people unable to say that men are on average taller than women because (I believe) they were afraid to say that some &quot;favorable&quot; outcome (tall) was a result of some component that could be genetic. Their argument was anecdotes of tall women, and saying there is no such thing as an &quot;average&quot; man or &quot;average&quot; woman.