The meeting point of differential privacy, accountability, interpretability, the tank detection story, clever horses in machine learning. Closely related: are the models what you would call fair?
Much work here; I understand little of it at the moment, but I keep needing to refer to papers here.
Here’s a thing that was so obvious I assumed it had already been done: KRPH17
Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from “What is the right fairness criterion?” to “What do we want to assume about the causal data generating process?” Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.
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- Barocas, S., & Selbst, A. D.(2016) Big Data’s Disparate Impact (SSRN Scholarly Paper No. ID 2477899). . Rochester, NY: Social Science Research Network
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- Choi, K., Fazekas, G., & Sandler, M. (2016) Explaining Deep Convolutional Neural Networks on Music Classification. ArXiv:1607.02444 [Cs].
- Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) Fairness Through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). New York, NY, USA: ACM DOI.
- Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 259–268). New York, NY, USA: ACM DOI.
- Hardt, M., Price, E., & Srebro, N. (2016) Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (pp. 3315–3323).
- Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017) Avoiding Discrimination through Causal Reasoning. ArXiv:1706.02744 [Cs, Stat].
- Lash, M. T., Lin, Q., Street, W. N., Robinson, J. G., & Ohlmann, J. (2016) Generalized Inverse Classification. ArXiv:1610.01675 [Cs, Stat].
- Lipton, Z. C.(2016) The Mythos of Model Interpretability. In arXiv:1606.03490 [cs, stat].
- Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2016) Universal adversarial perturbations. ArXiv:1610.08401 [Cs, Stat].
- Nguyen, A., Yosinski, J., & Clune, J. (2016) Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. ArXiv Preprint ArXiv:1602.03616.
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. (pp. 1135–1144). ACM Press DOI.
- Sweeney, L. (2013) Discrimination in Online Ad Delivery. Queue, 11(3), 10:10–10:29. DOI.
- Wisdom, S., Powers, T., Pitton, J., & Atlas, L. (2016) Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery. In Advances in Neural Information Processing Systems 29.
- Wu, X., & Zhang, X. (2016) Automated Inference on Criminality using Face Images. ArXiv:1611.04135 [Cs].
- Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013) Learning Fair Representations. In Proceedings of the 30th International Conference on Machine Learning (ICML-13) (pp. 325–333).