SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development
This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process pre...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:20.500.14342/4692 |
| Acceso en línea: | http://hdl.handle.net/20.500.14342/4692 https://doi.org/10.1007/s11948-023-00448-y |
| Access Level: | acceso abierto |
| Palabra clave: | Bias Artificial Intelligence Trustworthy AI Fairness Discrimination Pro-Ethical Design Intel·ligència artificial Confiança Imparcialitat Discriminació Ètica 070 17 |
| Sumario: | This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users. |
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