Details
| Original language | English |
|---|---|
| Pages (from-to) | 4-13 |
| Journal | SIGKDD Explorations |
| Volume | 23 |
| Issue number | 1 |
| Early online date | 29 May 2021 |
| Publication status | Published - Jun 2021 |
Abstract
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In: SIGKDD Explorations, Vol. 23, No. 1, 06.2021, p. 4-13.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Two Kinds of Discrimination in AI-Based Penal Decision-Making
AU - Hübner, Dietmar
PY - 2021/6
Y1 - 2021/6
N2 - The famous COMPAS case has demonstrated the difficulties in identifying and combatting bias and discrimination in AI-based penal decision-making. In this paper, I distinguish two kinds of discrimination that need to be addressed in this context. The first is related to the well-known problem of inevitable trade-offs between incompatible accounts of statistical fairness, while the second refers to the specific standards of discursive fairness that apply when basing human decisions on empirical evidence. I will sketch the essential requirements of non-discriminatory action within the penal sector for each dimension. Concerning the former, we must consider the relevant causes of perceived correlations between race and recidivism in order to assess the moral adequacy of alternative standards of statistical fairness, whereas regarding the latter, we must analyze the specific reasons owed in penal trials in order to establish what types of information must be provided when justifying court decisions through AI evidence. Both positions are defended against alternative views which try to circumvent discussions of statistical fairness or which tend to downplay the demands of discursive fairness, respectively.
AB - The famous COMPAS case has demonstrated the difficulties in identifying and combatting bias and discrimination in AI-based penal decision-making. In this paper, I distinguish two kinds of discrimination that need to be addressed in this context. The first is related to the well-known problem of inevitable trade-offs between incompatible accounts of statistical fairness, while the second refers to the specific standards of discursive fairness that apply when basing human decisions on empirical evidence. I will sketch the essential requirements of non-discriminatory action within the penal sector for each dimension. Concerning the former, we must consider the relevant causes of perceived correlations between race and recidivism in order to assess the moral adequacy of alternative standards of statistical fairness, whereas regarding the latter, we must analyze the specific reasons owed in penal trials in order to establish what types of information must be provided when justifying court decisions through AI evidence. Both positions are defended against alternative views which try to circumvent discussions of statistical fairness or which tend to downplay the demands of discursive fairness, respectively.
U2 - 10.1145/3468507.3468510
DO - 10.1145/3468507.3468510
M3 - Article
VL - 23
SP - 4
EP - 13
JO - SIGKDD Explorations
JF - SIGKDD Explorations
IS - 1
ER -