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In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing with Artificial Intelligence.
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The role of race in forecasts of violent crime
Berk, R., 2009 · 2009
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Kamiran, F., Calders, T., 2009 · 2009
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Causality
Pearl, J., 2009 · 2009
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Dawid, A.P., 2010 · 2010
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Fairness through awareness, in: Proceedings of the 3rd innovations in theoretical computer science conference, pp. 214–226
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R., 2012 · 2012
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Data preprocessing techniques for classification without discrimination
Kamiran, F., Calders, T., 2012 · 2012
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The hidden biases in big data
Crawford, K., 2013 · 2013
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Quantifying explainable discrimination and removing illegal discrimination in automated decision making
Kamiran, F., Žliobaitė, I., Calders, T., 2013 · 2013
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How big data is unfair: Understanding unintended sources of unfairness in data driven decision making (Medium)
Hardt, M., 2014 · 2014
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Causal discovery with continuous additive noise models
Peters, J., Mooij, J.M., Janzing, D., Schölkopf, B., 2014 · 2014
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Certifying and removing disparate impact, in: proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 259–268
Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S., 2015 · 2015
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The variational fair autoencoder
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R., 2015 · 2015
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The black box society
Pasquale, F., 2015 · 2015
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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., Kirchner, L., 2016 · 2016
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Big data’s disparate impact
Barocas, S., Selbst, A.D., 2016 · 2016
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On the (im)possibility of fairness
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., 2016 · 2016
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Equality of opportunity in supervised learning, in: Advances in neural information processing systems, pp. 3315–3323
Hardt, M., Price, E., Srebro, N., 2016 · 2016
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., Raghavan, M., 2016 · 2016
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Weapons of math destruction: How big data increases inequality and threatens democracy
O’neil, C., 2016 · 2016
Cited alongside, same era.
Causal inference in statistics: A primer
Pearl, J., Glymour, M., Jewell, N.P., 2016 · 2016
Cited alongside, same era.
A convex framework for fair regression
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., Roth, A., 2017 · 2017
Cited alongside, same era.
Optimized pre-processing for discrimination prevention, in: Advances in Neural Information Processing Systems, pp. 3992–4001
Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R., 2017 · 2017
Cited alongside, same era.
AI Now 2017 report
Campolo, A., Sanfilippo, M.R., Whittaker, M., Crawford, K., 2017 · 2017
Cited alongside, same era.
Fairness and Machine Learning
Barocas, S., Hardt, M., Narayanan, A., 2019 · 2019
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Path-specific counterfactual fairness, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7801–7808
Chiappa, S., 2019 · 2019
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Establishing the rules for building trustworthy ai
Floridi, L., 2019 · 2019
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Review of causal discovery methods based on graphical models
Glymour, C., Zhang, K., Spirtes, P., 2019 · 2019
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Ethics guidelines for trustworthy AI
High-level expert group on artificial intelligence (HLEG, AI), 2019 · 2019
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The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., Vayena, E., 2019 · 2019
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Chouldechova, A., 2017 · 2017
Cited alongside, same era.
UCI machine learning repository
Dua, D., Graff, C., 2017 · 2017
Cited alongside, same era.
Fairness in reinforcement learning, in: International conference on machine learning, PMLR. pp. 1617–1626
Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Roth, A., 2017 · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning, in: Advances in Neural Information Processing Systems, pp. 656–666
Kilbertus, N., Carulla, M.R., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B., 2017 · 2017
Cited alongside, same era.
Counterfactual fairness, in: Advances in neural information processing systems, pp. 4066–4076
Kusner, M.J., Loftus, J., Russell, C., Silva, R., 2017 · 2017
Cited alongside, same era.
McNamara, D., Ong, C.S., Williamson, R.C., 2017 · 2017
Cited alongside, same era.
Machine learning: The power and promise of computers that learn by example
Royal Society (Great Britain), 2017 · 2017
Cited alongside, same era.
An algorithm for removing sensitive information: application to race-independent recidivism prediction
Johndrow, J.E., Lum, K., et al., 2019 · 2019
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An empirical study of rich subgroup fairness for machine learning, in: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 100–109
Kearns, M., Neel, S., Roth, A., Wu, Z.S., 2019 · 2019
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This thing called fairness: disciplinary confusion realizing a value in technology
Mulligan, D.K., Kroll, J.A., Kohli, N., Wong, R.Y., 2019 · 2019
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On the apparent conflict between individual and group fairness, in: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp. 514–524
Binns, R., 2020 · 2020
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Fairlearn: A toolkit for assessing and improving fairness in AI
Bird, S., Dudík, M., Edgar, R., Horn, B., Lutz, R., Milan, V., Sameki, M., Wallach, H., Walker, K., 2020 · 2020
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Fliptest: fairness testing via optimal transport, in: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 111–121
Black, E., Yeom, S., Fredrikson, M., 2020 · 2020
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A snapshot of the frontiers of fairness in machine learning
Chouldechova, A., Roth, A., 2020 · 2020
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Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., Srikumar, M., 2020 · 2020
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A survey of learning causality with data: Problems and methods
Guo, R., Cheng, L., Li, J., Hahn, P.R., Liu, H., 2020 · 2020
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Wasserstein fair classification, in: Uncertainty in Artificial Intelligence, PMLR. pp. 862–872
Jiang, R., Pacchiano, A., Stepleton, T., Jiang, H., Chiappa, S., 2020 · 2020
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Codede bias
Kantayya, S., 2020 · 2020
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Fairness in machine learning, in: Recent Trends in Learning From Data. Springer, pp. 155–196
Oneto, L., Chiappa, S., 2020 · 2020
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Fairness with overlapping groups; a probabilistic perspective
Yang, F., Cisse, M., Koyejo, O.O., 2020 · 2020
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A survey on the explainability of supervised machine learning
Burkart, N., Huber, M.F., 2021 · 2021
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Translating principles into practices of digital ethics: Five risks of being unethical, in: Ethics, Governance, and Policies in Artificial Intelligence. Springer, pp. 81–90
Floridi, L., 2021 · 2021
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On the moral justification of statistical parity, in: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 747–757
Hertweck, C., Heitz, C., Loi, M., 2021 · 2021
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On the applicability of machine learning fairness notions
Makhlouf, K., Zhioua, S., Palamidessi, C., 2021 · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A., 2021 · 2021
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Algorithmic fairness: Choices, assumptions, and definitions
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., Lum, K., 2021 · 2021
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Group fairness: Independence revisited, in: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 129–137
Räz, T., 2021 · 2021
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Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts
The European Commission, 2021 · 2021
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Trustworthy artificial intelligence
Thiebes, S., Lins, S., Sunyaev, A., 2021 · 2021
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