To first order, advances in ML have been about passing a test or winning a game, which covers remarkably little territory @blaiseaguerahttps://twitter.com/math_rachel/status/1204806276347256833?s=20 …
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To first order, advances in ML have been about passing a test or winning a game, which covers remarkably little territory @blaiseaguerahttps://twitter.com/math_rachel/status/1204806276347256833?s=20 …
ML PhD turned cancer researcher Dana Pe’er shared that in biology, the goal is to understand, not just predict, and that outliers are often the most important (yet easily missed by many metrics)https://twitter.com/math_rachel/status/1204533105408823296 …
As a field, we are overly focused on prediction. It shouldn't be about the "right answers" but rather about asking meaningful questions-- from @Abebab talk on relational ethics (which won @black_in_ai i best paper award)https://twitter.com/math_rachel/status/1204107164203278337 …
Relatedly, I think overreliance on metrics and optimization is a fundamental weakness of many AI approaches. I wrote about this here:https://twitter.com/math_rachel/status/1176606580264951810 …
For people who weren't able to attend, would you have the names of some of those talks?
I've tagged/named the speakers, and the first two were keynotes at the main conference & the third was a contributed talk at Black in AI.
I wrote an article about the 7 years ago :)https://www.oreilly.com/radar/drivetrain-approach-data-products/ …
I want to echo this! #ResponsibleAI
Great points @math_rachel
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