@ruchowdh
@KayFButterfield
@johnchavens
@vdignum
@jonniepenn
@j2bryson
@tabithagold
@maria_axente
@robmccargow
@Debbie_Hellman
@fatconference
We also associate “explainability” with ideas like “contestability”, “autonomy”, “recourse.” We should speak about the latter ideas separately since explanations in ML guarantee none of these things.
Good thread here:
Explanations are convenient for large firms in algorithms, AI, ML
They don't give users control
They don't shake-up power relations
They don't shine light on systems as a whole
It's irresponsible of researchers to jump on the explanation bandwagon without being critical of them
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If you were to make a glossary of
#AI
and
#ethics
terms, what would they be?
@KayFButterfield
@johnchavens
@vdignum
@jonniepenn
@j2bryson
@tabithagold
@maria_axente
@robmccargow
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@ruchowdh
@KayFButterfield
@johnchavens
@vdignum
@jonniepenn
@j2bryson
@tabithagold
@maria_axente
@robmccargow
We should try to use “justice” instead of “fairness” to make it clear that we’re not talking about “parity.”
There’s a nice story about this in the talk by
@Debbie_Hellman
at
@fatconference
last year.
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@berkustun
@ruchowdh
@KayFButterfield
@johnchavens
@vdignum
@jonniepenn
@tabithagold
@maria_axente
@robmccargow
@Debbie_Hellman
@fatconference
Sounds similar to my recent line — accountability is what matters. eg
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