Algorithmic Fairness
Choices, Assumptions, and Definitions
Dados Bibliográficos
| ID | 23375613 |
|---|---|
| Autores | Shira Mitchell (Google (United States)), Eric Potash (0000-0003-4658-9802, Google (United States)), Solon Barocas (0000-0003-4577-466X, Microsoft (United States)), Alexander D'Amour (Google Research, Cambridge, Massachusetts 02124, USA), Alexander D’Amour (0000-0001-7984-3366, Google (United States)), Kristian Lum (0000-0003-2637-5612, Google (United States)) |
| Ano | 2021 |
| Volume | 8 |
| Fascículo | 1 |
| Páginas | 141-163 |
| Data de publicação | 2021-03-07 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Annual Review of Statistics and Its Application (JOURNAL) |
| Identificadores do periódico | ISSN: 2326-8298 • E-ISSN: 2326-831X |
| Editora | Annual Reviews (PUBLISHER • US) |
| DOI | 10.1146/annurev-statistics-042720-125902 |
| OpenAlex | W3101206394 |
| Idioma | EN |
| Citações recebidas | 83 |
| Referências citadas | 56 |
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-needed order. First, we explicate the various choices and assumptions made—often implicitly—to justify the use of prediction-based decision-making. Next, we show how such choices and assumptions can raise fairness concerns and we present a notationally consistent catalog of fairness definitions from the literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision-making.
Data science · Economics · Field (mathematics) · Management science · Notation · Operations research · Order (exchange) · Terminology · Adversarial Robustness in Machine Learning · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Mathematics
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| Obras citantes distintas | 83 |
|---|---|
| Citações por ano | 16,6 |
| Intervalo de citações | 2021 - 2026 (6) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 78 |