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pro vyhledávání: '"fair machine learning"'
Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair". Real-world data, however, are typically plagued by various measurement biases and other violated assumptio
Externí odkaz:
http://arxiv.org/abs/2410.09600
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning. In these settings, predictive models are developed specifically to satisfy pre-defined notions of fairness. Here, we study t
Externí odkaz:
http://arxiv.org/abs/2404.09847
Autor:
Cousins, Cyrus
The original position or veil of ignorance argument of John Rawls, perhaps the most famous argument for egalitarianism, states that our concept of fairness, justice, or welfare should be decided from behind a veil of ignorance, and thus must consider
Externí odkaz:
http://arxiv.org/abs/2404.06703
As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machi
Externí odkaz:
http://arxiv.org/abs/2407.15006
Akademický článek
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Autor:
Zezulka, Sebastian, Genin, Konstantin
Deploying an algorithmically informed policy is a significant intervention in society. Prominent methods for algorithmic fairness focus on the distribution of predictions at the time of training, rather than the distribution of social goods that aris
Externí odkaz:
http://arxiv.org/abs/2401.14438
Akademický článek
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The definition and implementation of fairness in automated decisions has been extensively studied by the research community. Yet, there hides fallacious reasoning, misleading assertions, and questionable practices at the foundations of the current fa
Externí odkaz:
http://arxiv.org/abs/2306.01417
Publikováno v:
Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), CEUR Workshop Proceedings, https://ceur-ws.org/Vol-3523/
A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include
Externí odkaz:
http://arxiv.org/abs/2307.12797
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1519-1538 (2023)
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing b
Externí odkaz:
https://doaj.org/article/018e7c40286944bd89ca20b50af98b43
