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14 - Differences That Make a Difference

Computational Profiling and Fairness to Individuals*

from Part IV - Fairness and Nondiscrimination in AI Systems

Published online by Cambridge University Press:  28 October 2022

Silja Voeneky
Affiliation:
Albert-Ludwigs-Universität Freiburg, Germany
Philipp Kellmeyer
Affiliation:
Medical Center, Albert-Ludwigs-Universität Freiburg, Germany
Oliver Mueller
Affiliation:
Albert-Ludwigs-Universität Freiburg, Germany
Wolfram Burgard
Affiliation:
Technische Universität Nürnberg

Summary

The philosopher Wilfried Hinsch focuses on statistical discrimination by means of computational profiling. He defines statistical profiling as an estimate of what individuals will do by considering the group of people they can be assigned to. The author explores which criteria of fairness and justice are appropriate for the assessment of computational profiling. According to Hinsch, grounds of discrimination such as gender or ethnicity do not explain when or why it is wrong to discriminate. Thus, Hinsch argues that discrimination constitutes a rule-guided social practice that imposes unreasonable burdens on specific people. He argues that, on the one hand, statistical profiling is a part of human nature and not by itself wrongful discrimination. However, on the other hand, even statistically correct profiles can be unacceptable considering reasons of procedural fairness or substantive justice. Because of this, Hinsch suggests a fairness index for profiles to determine procedural fairness; and argues that because AI systems do not rely on human stereotypes or rather limited data, computational profiling may be a better safeguard of fairness than humans.

Information

Figure 0

Figure 14.1 Fairness-index for statistical profiling based on measures for the under- and over-inclusiveness of profiles (The asymmetry of the areas C and D is meant to indicate that we reasonably expect statistical profiles to yield more true than false positives.)

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