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linnet taylor 30. sij
explainability is mainly used for internal debugging, its goals are not clearly defined, and it has serious technical limitations. 'we suggest everyone cast doubt on those who are claiming to work with explainability technologies' at
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Maryammjd 29. sij
We are at ACM . The child care service is amazing. We need to see more of this in academic conferences.
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linnet taylor 30. sij
- if we look beyond fairness to justice we should often not be using ML bc stats methods don't address individual merits. what does this tell us about using on life-and-death issues such as asylum, sentencing, welfare allocation? ()
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Latifa Jackson 15. lis
Odgovor korisniku/ci @dr_nickiw
This is exciting news, Congratulations!!! We just wrote a paper for on a similar topic called "No Computation without Representation: Avoiding data and algorithm biases through diversity." I would love to read your paper and your eventual book. Keep up the good work!
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linnet taylor 30. sij
at : bc disadvantage determines how people are conceptualised & monitored for policy, any application of formal fairness measures will increase substantive inequality'. The 'we' in 'we measure x' tends to be inherently unjust.
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Flora Salim 28. sij
The last speaker today noted aside from us standing in between him and reception, human labeling / annotation is messy! “No time for methods”, not only bec it’s 8 mins talk, but ppl focus more on results than legitimacy and quality of labels
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Ivan Humble 29. sij
presented at the in yesterday supports the notion that ’s platform is playing a role in users via to -
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Flora Salim 28. sij
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linnet taylor 30. sij
Thanks , , salvatore, and for being supportive, kind and welcoming to all us newcomers to .
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