Academic Journal
fairadapt: Causal Reasoning for Fair Data Preprocessing
| العنوان: | fairadapt: Causal Reasoning for Fair Data Preprocessing |
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| المؤلفون: | Plečko, Drago, Bennett, Nicolas, Meinshausen, Nicolai |
| المصدر: | Journal of Statistical Software, 110 (4) |
| بيانات النشر: | American Statistical Association |
| سنة النشر: | 2024 |
| المجموعة: | ETH Zürich Research Collection |
| مصطلحات موضوعية: | algorithmic fairness, causal inference, machine learning |
| الوصف: | Machine learning algorithms are useful for various prediction tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes. This realization gave rise to the field of fair machine learning, which aims to recognize, quantify and ultimately mitigate such algorithmic bias. This manuscript describes the R package fairadapt, which implements a causal inference preprocessing method. By making use of a causal graphical model alongside the observed data, the method can be used to address hypothetical questions of the form "What would my salary have been, had I been of a different gender/race?". Such individual level counterfactual reasoning can help eliminate discrimination and help justify fair decisions. We also discuss appropriate relaxations which assume that certain causal pathways from the sensitive attribute to the outcome are not discriminatory. ; ISSN:1548-7660 |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/application/pdf |
| اللغة: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/wos/001308869900001; http://hdl.handle.net/20.500.11850/694309 |
| DOI: | 10.3929/ethz-b-000694309 |
| الاتاحة: | https://hdl.handle.net/20.500.11850/694309 https://doi.org/10.3929/ethz-b-000694309 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/3.0/ ; Creative Commons Attribution 3.0 Unported |
| رقم الانضمام: | edsbas.B49AC7B6 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3929/ethz-b-000694309 |
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