Algorithmic Fairness
Choices, Assumptions, and Definitions
Datos 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)) |
| Año | 2021 |
| Volumen | 8 |
| Número | 1 |
| Páginas | 141-163 |
| Fecha de publicación | 2021-03-07 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Annual Review of Statistics and Its Application (JOURNAL) |
| Identificadores de la revista | ISSN: 2326-8298 • E-ISSN: 2326-831X |
| Editorial | Annual Reviews (PUBLISHER • US) |
| DOI | 10.1146/annurev-statistics-042720-125902 |
| OpenAlex | W3101206394 |
| Idioma | EN |
| Citas recibidas | 83 |
| Referencias 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
Ethical trade-offs in AI for mental health
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Defensive Attribution of AI Fairness
Assessing the predictive validity of a risk assessment instrument for repeat victimization in the Netherlands using prior police contacts
Policy advice and best practices on bias and fairness in AI
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You are where you live? Evaluating the racial and ethnic (mis)representation in geodemographic classification
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Counter-productivity and suspicion
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Qualitative exploration of child welfare workers’ decision-making experiences and perspectives on fairness
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What’s Impossible about Algorithmic Fairness
Fairness Hacking
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How people connect fairness and equity when they talk about data uses
Justicia algorítmica y autodeterminación deliberativa
Classification algorithms and social outcomes
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The Moral Affordances of Construing People as Cases
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Artificial Intelligence and Democracy
Clinical algorithms, racism, and "fairness" in healthcare
Diversity in sociotechnical machine learning systems
Algorithmic Fairness and Statistical Discrimination
Designing Better Access to Education? Unified Enrollment, School Choice, and the Limits of Algorithmic Fairness in New Orleans School Admissions
Algorithmic bias
Causal Inference for Statistics, Social, and Biomedical Sciences
Causality
Model Cards for Model Reporting
Fairness and Abstraction in Sociotechnical Systems
Layers of Bias
Big Data's Disparate Impact
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables
Algorithmic Decision Making and the Cost of Fairness
Fair Prediction with Disparate Impact
Eddie Murphy and the Dangers of Counterfactual Causal Thinking About Detecting Racial Discrimination
A Theory of Justice
Demarginalizing the Intersection of Race and Sex
Causal Effects of Perceived Immutable Characteristics
Methodological considerations relevant to discrimination in employment testing
Risk, Race, and Recidivism
Automating Inequality
Critical analysis of the statistical and ethical implications of various definitions of test bias
Fairness in Criminal Justice Risk Assessments
| Obras citantes distintas | 83 |
|---|---|
| Citas por año | 16,6 |
| Intervalo de citas | 2021 - 2026 (6) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 78 |