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Rethinking Fairness Metrics for Multi-Class and Multi-Group Assessment: Not Everything is Binary — arXiv:2611.03257

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03257
License (as archived): CC0-1.0
Archived: 2026-11-03T10:38:11.493Z
SHA-256 of the archived text: 4f92ee459f0ea3cb3fb264c3f0106902197ce01b9cb8b6f46543b3765091cda4
Stable URL of this page: https://metraipolis.com/documento/dc_342d0fdcd0bd

How to cite

Rethinking Fairness Metrics for Multi-Class and Multi-Group Assessment: Not Everything is Binary — arXiv:2611.03257 — arXiv (en), https://arxiv.org/abs/2611.03257 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_342d0fdcd0bd (Metraipolis archive dc_342d0fdcd0bd, sha256 4f92ee459f0ea3cb3fb264c3f0106902197ce01b9cb8b6f46543b3765091cda4)

BibTeX

@misc{dc342d0fdcd0bd,
  title = {Rethinking Fairness Metrics for Multi-Class and Multi-Group Assessment: Not Everything is Binary — arXiv:2611.03257},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03257},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_342d0fdcd0bd; sha256 4f92ee459f0ea3cb3fb264c3f0106902197ce01b9cb8b6f46543b3765091cda4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03257. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Artificial Intelligence, and Machine Learning in general, is becoming pervasive across all domains and disciplines. By means of this data-driven approach, algorithms can learns complex task just by learning from samples. However, if the data-set is biased, they might lead to the development of unfair computer systems, that is, characterised by a prejudice towards specific individuals or groups, which might get an unmotivated output from the system. This circumstance lead to the development of multiple metrics designed to quantify the level of unfairness of an algorithm. However, a generalised assumption in the literature is that the sensitive features and the output labels are binary. When the data is not binary, it is forcefully binarized using a one-versus-the-rest simplification. In this work, we discuss the limitations of existing metrics and propose novel, mathematically sound, and generalized multi-group and multi-class formulations of the metrics. Ultimately, our results highlight the empirical assessment distortion caused by binarization strategies, which reveals the limitations of binary fairness metrics in the multi class setting.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03257 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.939Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).