TY - JOUR
T1 - Algorithmic and Non-Algorithmic Fairness
T2 - Should We Revise our View of the Latter Given Our View of the Former?
AU - Lippert-Rasmussen, Kasper
PY - 2025/4
Y1 - 2025/4
N2 - In the US context, critics of court use of algorithmic risk prediction algorithms have argued that COMPAS involves unfair machine bias because it generates higher false positive rates of predicted recidivism for black offenders than for white offenders. In response, some have argued that algorithmic fairness concerns, either also or only, calibration across groups–roughly, that a score assigned to different individuals by the algorithm involves the same probability of the individual having the target property across different groups of individuals–and that, for mathematical reasons, it is virtually impossible to equalize false positive rates without impairing the calibration. I argue that in standard non-algorithmic contexts, such as hirings, we do not think that lack of calibration entails unfair bias, and that it is difficult to see why algorithmic contexts, as it were, should differ fairness-wise from non-algorithmic ones in this respect. Hence, we should reject the view that calibration is necessary for fairness in an algorithmic context.
AB - In the US context, critics of court use of algorithmic risk prediction algorithms have argued that COMPAS involves unfair machine bias because it generates higher false positive rates of predicted recidivism for black offenders than for white offenders. In response, some have argued that algorithmic fairness concerns, either also or only, calibration across groups–roughly, that a score assigned to different individuals by the algorithm involves the same probability of the individual having the target property across different groups of individuals–and that, for mathematical reasons, it is virtually impossible to equalize false positive rates without impairing the calibration. I argue that in standard non-algorithmic contexts, such as hirings, we do not think that lack of calibration entails unfair bias, and that it is difficult to see why algorithmic contexts, as it were, should differ fairness-wise from non-algorithmic ones in this respect. Hence, we should reject the view that calibration is necessary for fairness in an algorithmic context.
UR - http://www.scopus.com/inward/record.url?scp=105001080799&partnerID=8YFLogxK
U2 - 10.1007/s10982-024-09505-4
DO - 10.1007/s10982-024-09505-4
M3 - Journal article
AN - SCOPUS:105001080799
SN - 0167-5249
VL - 44
SP - 155
EP - 179
JO - Law and Philosophy
JF - Law and Philosophy
IS - 2
ER -