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A growing body of literature in fairness-aware machine learning (fairML) aims to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods to ensure that trained ML models achieve low scores on these metrics.
Marrying Fairness and Explainability in Supervised Learning
Grabowicz, P. A., Perello, N., and Mishra, A. (2022) · 1916
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Fair inference on outcomes
Nabi, R., and Shpitser, I. (2018) · 1940
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Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Hebert-Johnson, U., Kim, M., Reingold, O., and Rothblum, G. (2018) · 1948
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Employment Tests and Discriminatory Hiring
Guion, R. M. (1966) · 1966
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Concepts of Culture-Fairness
Thorndike, R. L. (1971) · 1971
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Effects of Affirmative Action in Medical Schools
Keith, S. N., Bell, R. M., Swanson, A. G., and Williams, A. P. (1985) · 1985
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An Economic Argument for Affirmative Action
Foster, D. P., and Vohra, R. V. (1992) · 1992
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Will Affirmative-Action Policies Eliminate Negative Stereotypes?
Coate, S., and Loury, G. (1993) · 1993
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A Theory of Justice
Rawls, J. A. (2003) · 2003
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Decentralizing Equality of Opportunity and Issues Concerning the Equality of Educational Opportunity
Calsamiglia, C. (2005) · 2005
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Best Practices or Best Guesses? Assessing the Efficacy of Corporate Affirmative Action and Diversity Policies
Kalev, A., Dobbin, F., and Kelly, E. (2006) · 2006
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Quantile Regression Forests
Meinshausen, N. (2006) · 2006
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Discrimination-aware Data Mining
Pedreshi, D., Ruggieri, S., and Turini, F. (2008) · 2008
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The Nicomachean ethics (book V)
Aristotle (2009) · 2009
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Causality: Models, Reasoning and Inference
Pearl, J. (2009) · 2009
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Fairness Through Awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012) · 2012
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Gender Medicine: A Task for the Third Millennium
Baggio, G., Corsini, A., Floreani, A., Giannini, S., and Zagonel, V. (2013) · 2013
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Machine Bias: There’s Software Used Across the Country to Predict Future Criminals, and It’s Biased Against Blacks
Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016) · 2016
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EUR-Lex - 32016R0679 - EN - EUR-Lex.
EU (2016) · 2016
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Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights.
Executive Office of the President, Muñoz, C., Smith, M., and Patil, D. (2016) · 2016
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How We Analyzed the COMPAS Recidivism Algorithm
Larson, J., Mattu, S., Kirchner, L., and Angwin, J. (2016) · 2016
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Causal Inference in Statistics
Pearl, J., Glymour, M., and Jewell, N. P. (2016) · 2016
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Philosophical Perspectives on Fairness in Educational Assessment
Zwick, R., and Dorans, N. J. (2016) · 2016
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Chapter 3. What Is Fairness?
Dator, J. (2017) · 2017
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On Formalizing Fairness in Prediction With Machine Learning
Gajane, P., and Pechenizkiy, M. (2017) · 2017
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Avoiding Discrimination through Causal Reasoning
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B. (2017) · 2017
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Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017) · 2017
Cited alongside, same era.
Counterfactual Fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R. (2017) · 2017
Cited alongside, same era.
A Reductions Approach to Fair Classification
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H. (2018) · 2018
Cited alongside, same era.
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
Barabas, C., Virza, M., Dinakar, K., Ito, J., and Zittrain, J. (2018) · 2018
Cited alongside, same era.
Fairness in Machine Learning: Lessons from Political Philosophy
Binns, R. (2018) · 2018
Cited alongside, same era.
Fair Risk Assessments: A Precarious Approach for Criminal Justice Reform
Green, B. (2018) · 2018
Cited alongside, same era.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., and Roth, A. (2021) · 2021
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Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
Chikahara, Y., Sakaue, S., Fujino, A., and Kashima, H. (2021) · 2021
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The (im)possibility of fairness: different value systems require different mechanisms for fair decision making
Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S. (2021) · 2021
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On Statistical Criteria of Algorithmic Fairness
Hedden, B. (2021) · 2021
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The Use and Misuse of Counterfactuals in Ethical Machine Learning
Kasirzadeh, A., and Smart, A. (2021) · 2021
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Fairness, Equality, and Power in Algorithmic Decision-Making
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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018) · 2018
Cited alongside, same era.
Theories of Distributive Justice
Roemer, J. E. (2018) · 2018
Cited alongside, same era.
Fairness Definitions Explained
Verma, S., and Rubin, J. (2018) · 2018
Cited alongside, same era.
Fairness and Machine Learning
Barocas, S., Hardt, M., and Narayanan, A. (2019) · 2019
Cited alongside, same era.
Künstliche Intelligenz und Diskriminierung
Beck, S., Grunwald, A., Jacob, K., and Matzner, T. (2019) · 2019
Cited alongside, same era.
Path-Specific Counterfactual Fairness
Chiappa, S. (2019) · 2019
Cited alongside, same era.
Kasy, M., and Abebe, R. (2021) · 2021
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Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?
Kuppler, M., Kern, C., Bach, R. L., and Kreuter, F. (2021) · 2021
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Formalising Trade-offs Beyond Algorithmic Fairness: Lessons from Ethical Philosophy and Welfare Economics
Lee, M. S. A., Floridi, L., and Singh, J. (2021) · 2021
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Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness
Long, R. (2021) · 2021
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Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., and Lum, K. (2021) · 2021
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Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Pan, W., Cui, S., Bian, J., Zhang, C., and Wang, F. (2021) · 2021
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Fairness in Ranking under Uncertainty
Singh, A., Kempe, D., and Joachims, T. (2021) · 2021
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A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Suresh, H., and Guttag, J. V. (2021) · 2021
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Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law
Wachter, S., Mittelstadt, B., and Russell, C. (2021) · 2021
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Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
Black, E., Elzayn, H., Chouldechova, A., Goldin, J., and Ho, D. (2022) · 2022
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Fairness
Cambridge Dictionary (2022) · 2022
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Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness
Green, B. (2022) · 2022
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Are “Intersectionally Fair” AI Algorithms Really Fair to Women of Color? A Philosophical Analysis
Kong, Y. (2022) · 2022
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A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2022) · 2022
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The Long Arc of Fairness: Formalisations and Ethical Discourse
Schwöbel, P., and Remmers, P. (2022) · 2022
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Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
Bothmann, L., Dandl, S., and Schomaker, M. (2023) · 2023
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Algorithmic Fairness Criteria as Evidence
Fleisher, W. (2023) · 2023
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Fairness in Machine Learning: A Survey
Caton, S., and Haas, C. (2024) · 2024
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fairadapt: Causal reasoning for fair data preprocessing
Plečko, D., Bennett, N., and Meinshausen, N. (2024) · 2024
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Is Calibration a Fairness Requirement? An Argument from the Point of View of Moral Philosophy and Decision Theory
Loi, M., and Heitz, C. (2022) · 2034
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