Fairness in Machine Learning¶
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of Machine Learning Research, 81, 149-159.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Realized vs Possible Outcomes
- Sen's capability-versus-functioning distinction transferred from development economics into AI fairness, as the realization that outcome equality does not imply opportunity equality, with some fairness frameworks explicitly invoking Sen.
This sourceConnects fairness frameworks to political philosophy including Sen's capability approach, distinguishing outcome equality from opportunity equality.
- Sen's capability-versus-functioning distinction transferred from development economics into AI fairness, as the realization that outcome equality does not imply opportunity equality, with some fairness frameworks explicitly invoking Sen.
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