Rezultati pretraživanja
  1. 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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  2. 29. sij

    We are at ACM . The child care service is amazing. We need to see more of this in academic conferences.

  3. 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? ()

  4. 15. lis 2019.
    Odgovor korisniku/ci

    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!

  5. 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.

  6. 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

  7. 28. sij
  8. 29. sij
  9. 30. sij

    Thanks , , salvatore, and for being supportive, kind and welcoming to all us newcomers to .

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