Reframing the conversation from black defendants being penalized but rather white defendants being selectively forgiven
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An algorithm to determine car insurance premiums raises premiums by risk when the driver lives in a predominantly black zipcode
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But for whatever reason, premiums for white drivers drop off despite higher risk: algorithmic forgiveness
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So the biased outcomes for black people are ultimately defended as being “logical” and “fair” and deterministic—
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When the issue is that white people are *systematically* given a pass where black people aren’t
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It reminds me a lot about the conversation around women in tech—
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Women in all sorts of male dominated professions receive harsher performance reviews compare to their male counterparts
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When challenged, there’s always *some* reason why the woman isn’t that good of a worker or is subpar
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But the forgiveness being granted to her male colleagues rarely enters the conversation.
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But back to the original example: The idea that the error baked into these “deterministic” systems is on the side of whiteness is compelling
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I like the frame of “forgiveness” too because we spend far too much time using words like “disparate impact”
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Useful phrase but it doesn’t have the same gut impact as “forgiveness” — why is an algorithm, of all things, forgiving along racial lines?
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Good takeaways from that whole session. Me, Emily Gorcenski, and Gillian Crampton-Smith are the next block at 3:15.
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Worth reading some of the rebuttals to her ProPublica article to get a rounded view. Complex topic. https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html … … is good.
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And here is a much more in depth academic paper on the topic: http://buff.ly/2uTPGhy
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@Prof_Bruckner, this seems relevant to your interests. -
Thank you!
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Le chargement semble prendre du temps.
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