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|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Group fairness in prediction-based decision making : from moral assessment to implementation|
|Proceedings:||Proceedings 2022 9th Swiss Conference on Data Science (SDS)|
|Conference details:||9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022|
|Publisher / Ed. Institution:||IEEE|
|Subjects:||Algorithmic fairness; Prediction-based decision making; Ethical fairness principle; Group fairness criteria; Philosophy; Distributive justice|
|Subject (DDC):||005: Computer programming, programs and data |
658.403: Decision making, information management
|Abstract:||Ensuring fairness of prediction-based decision making is based on statistical group fairness criteria. Which one of these criteria is the morally most appropriate one depends on the context, and its choice requires an ethical analysis. In this paper, we present a step-by-step procedure integrating three elements: (a) a framework for the moral assessment of what fairness means in a given context, based on the recently proposed general principle of “Fair equality of chances” (FEC) (b) a mapping of the assessment's results to established statistical group fairness criteria, and (c) a method for integrating the thus-defined fairness into optimal decision making. As a second contribution, we show new applications of the FEC principle and show that, with this extension, the FEC framework covers all types of group fairness criteria: independence, separation, and sufficiency. Third, we introduce an extended version of the FEC principle, which additionally allows accounting for morally irrelevant elements of the fairness assessment and links to well-known relaxations of the fairness criteria. This paper presents a framework to develop fair decision systems in a conceptually sound way, combining the moral and the computational elements of fair prediction-based decision-making in an integrated approach. Data and code to reproduce our results are available at https://github.com/joebaumann/fair-prediction-based-decision-making.|
|Fulltext version:||Accepted version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Published as part of the ZHAW project:||Algorithmic Fairness in data-based decision making: Combining ethics and technology|
|Appears in collections:||Publikationen School of Engineering|
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|2022_Baumann_Heitz_Group-fairness-prediction-based-decision-making_IEEE.pdf||Accepted Version||242.6 kB||Adobe PDF|
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Baumann, J., & Heitz, C. (2022). Group fairness in prediction-based decision making : from moral assessment to implementation [Conference paper]. Proceedings 2022 9th Swiss Conference on Data Science (SDS), 19–25. https://doi.org/10.1109/SDS54800.2022.00011
Baumann, J. and Heitz, C. (2022) ‘Group fairness in prediction-based decision making : from moral assessment to implementation’, in Proceedings 2022 9th Swiss Conference on Data Science (SDS). IEEE, pp. 19–25. Available at: https://doi.org/10.1109/SDS54800.2022.00011.
J. Baumann and C. Heitz, “Group fairness in prediction-based decision making : from moral assessment to implementation,” in Proceedings 2022 9th Swiss Conference on Data Science (SDS), 2022, pp. 19–25. doi: 10.1109/SDS54800.2022.00011.
Baumann, Joachim, and Christoph Heitz. “Group Fairness in Prediction-Based Decision Making : From Moral Assessment to Implementation.” Proceedings 2022 9th Swiss Conference on Data Science (SDS), IEEE, 2022, pp. 19–25, https://doi.org/10.1109/SDS54800.2022.00011.
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