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...
| Published in: | Science & Engineering Ethics Vol. 29; no. 4; pp. 1 - 20 |
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| Main Authors: | , |
| Format: | Article |
| Published: |
Springer Nature
Aug2023
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| Online Access: | View this record in EBSCOhost |