From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making
Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the con...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-10-01
|
| Series: | Frontiers in Sociology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsoc.2022.883999/full |
| _version_ | 1811194683968192512 |
|---|---|
| author | Matthias Kuppler Christoph Kern Christoph Kern Ruben L. Bach Frauke Kreuter Frauke Kreuter |
| author_facet | Matthias Kuppler Christoph Kern Christoph Kern Ruben L. Bach Frauke Kreuter Frauke Kreuter |
| author_sort | Matthias Kuppler |
| collection | DOAJ |
| description | Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making. |
| first_indexed | 2024-04-12T00:30:48Z |
| format | Article |
| id | doaj.art-53530bf0c49543a2903e936506017dbb |
| institution | Directory Open Access Journal |
| issn | 2297-7775 |
| language | English |
| last_indexed | 2024-04-12T00:30:48Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sociology |
| spelling | doaj.art-53530bf0c49543a2903e936506017dbb2022-12-22T03:55:20ZengFrontiers Media S.A.Frontiers in Sociology2297-77752022-10-01710.3389/fsoc.2022.883999883999From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-makingMatthias Kuppler0Christoph Kern1Christoph Kern2Ruben L. Bach3Frauke Kreuter4Frauke Kreuter5Department of Social Sciences, University of Siegen, Siegen, GermanySchool of Social Sciences, University of Mannheim, Mannheim, GermanyJoint Program in Survey Methodology, University of Maryland, College Park, MD, United StatesSchool of Social Sciences, University of Mannheim, Mannheim, GermanyJoint Program in Survey Methodology, University of Maryland, College Park, MD, United StatesDepartment of Statistics, LMU Munich, Munich, GermanyPrediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making.https://www.frontiersin.org/articles/10.3389/fsoc.2022.883999/fullautomationpredictionalgorithmfairnessdistributive justice |
| spellingShingle | Matthias Kuppler Christoph Kern Christoph Kern Ruben L. Bach Frauke Kreuter Frauke Kreuter From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making Frontiers in Sociology automation prediction algorithm fairness distributive justice |
| title | From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making |
| title_full | From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making |
| title_fullStr | From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making |
| title_full_unstemmed | From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making |
| title_short | From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making |
| title_sort | from fair predictions to just decisions conceptualizing algorithmic fairness and distributive justice in the context of data driven decision making |
| topic | automation prediction algorithm fairness distributive justice |
| url | https://www.frontiersin.org/articles/10.3389/fsoc.2022.883999/full |
| work_keys_str_mv | AT matthiaskuppler fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking AT christophkern fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking AT christophkern fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking AT rubenlbach fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking AT fraukekreuter fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking AT fraukekreuter fromfairpredictionstojustdecisionsconceptualizingalgorithmicfairnessanddistributivejusticeinthecontextofdatadrivendecisionmaking |