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Machine Learning systems are increasingly prevalent across healthcare, law enforcement, and finance but often operate on historical data, which may carry biases against certain demographic groups.
Lsac national longitudinal bar passage study. lsac research report series
Wightman, L. F · 1998
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Uci machine learning repository, 2007
Asuncion, A. and Newman, D · 2007
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Identifiability of causal graphs using functional models
Peters, J., Mooij, J., Janzing, D., and Schölkopf, B · 2012
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Causal discovery with continuous additive noise models
Peters, J., Mooij, J. M., Janzing, D., and Schölkopf, B · 2014
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Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H · 2018
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Dowhy: An end-to-end library for causal inference
Sharma, A. and Kiciman, E · 2020
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Transformers can do bayesian inference
Müller, S., Hollmann, N., Arango, S. P., Grabocka, J., and Hutter, F · 2021
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A clarification of the nuances in the fairness metrics landscape
Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I. G., and Cosentini, A. C · 2022
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Tabpfn: A transformer that solves small tabular classification problems in a second
Hollmann, N., Müller, S., Eggensperger, K., and Hutter, F · 2022
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Amortized inference for causal structure learning, 2022
Lorch, L., Sussex, S., Rothfuss, J., Krause, A., and Schölkopf, B · 2022
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Plecko, D. and Bareinboim, E · 2022
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Fairness and machine learning: Limitations and opportunities
Barocas, S., Hardt, M., and Narayanan, A · 2023
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Learning for counterfactual fairness from observational data
Ma, J., Guo, R., Zhang, A., and Li, J · 2023
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