Journal Article10.1145/3422648.3422656
Technical Perspective: Database Repair Meets Algorithmic Fairness
Lise Getoor
- 04 Sep 2020
- Vol. 49, Iss: 1, pp 33-33
2
TL;DR: There has been an explosion of interest in fairness in machine learning in large part, motivated by societal issues highlighted in a string of well publicized cases such as gender biased job recommendation and racially biased criminal risk prediction algorithms.
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Abstract: There has been an explosion of interest in fairness in machine learning. In large part, this has been motivated by societal issues highlighted in a string of well publicized cases such as gender biased job recommendation and racially biased criminal risk prediction algorithms. Both the recognition of the potential disparate impacts of machine learning due to historical bias in the data and the realization of how algorithmic decision making can exaggerate existing structural inequities has become increasingly well known.
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Citations
Fairness & friends in the data science era
TL;DR: In this article , the authors focus on the specific data processing tasks for which nondiscrimination solutions have been proposed, by focusing on specific non-discrimination tasks for data processing pipelines.
References
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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