One of the things that I think is really important is that we’re paying attention to how we might be able to recuperate and recover from these kinds of practices. So rather than thinking of this as just a temporary kind of glitch, in fact I’m going to show you several of these glitches and maybe we might see a pattern.
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I’ve experienced first hand the challenges of trying to correct misinformation, and in part my academic research builds on that experience and tries understand why it was that so much of what we did at Spinsanity antagonized even those people who were interested enough to go to a fact-checking web site.
What is it about our brains that makes facts so challenging, so odd and threatening? Why do we sometimes double down on false beliefs? And maybe why do some of us do it more than others?
The question is what are we doing in the industry, or what is the machine learning research community doing, to combat instances of algorithmic bias? So I think there is a certain amount of good news, and it’s the good news that I wanted to focus on in my talk today.
I wouldn’t be surprised to find out that many of us here today like to see our work as a continuation of say the Tech Model Railroad Club or the Homebrew Computer Club, and certainly the terminology and the values of this conference, like open source for example, have their roots in that era. As a consequence it’s easy to interpret any criticism of the hacker ethic—which is what I’m about to do—as a kind of assault.