The meeting point of differential privacy, tank detection, clever horses in machine learning, and the connection to discrimination against minorities.
Much work here; I understand little of it at the moment, but I keep needing to refer to papers here.
Most notably, lime is the software for automated explanation of classifications, (RiSG16) and is cute. See their blog post.
Barocas, S., & Selbst, A. D.(2014). Big Data’s Disparate Impact (SSRN Scholarly Paper No. ID 2477899). Rochester, NY: Social Science Research Network. Online.
Homework problem: What can the following model tell us? Automated Inference on Criminality using Face Images (WuZh16)
[…]we find some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle. Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds. The variation among criminal faces is significantly greater than that of the non-criminal faces. The two manifolds consisting of criminal and non-criminal faces appear to be concentric, with the non-criminal manifold lying in the kernel with a smaller span, exhibiting a law of normality for faces of non-criminals. In other words, the faces of general law-biding public have a greater degree of resemblance compared with the faces of criminals, or criminals have a higher degree of dissimilarity in facial appearance than normal people.
Oh, and what would you be happy with your local law enforcement authority taking home from this?
- Aggarwal, C. C., & Yu, P. S.(2008) A General Survey of Privacy-Preserving Data Mining Models and Algorithms. In C. C. Aggarwal & P. S. Yu (Eds.), Privacy-Preserving Data Mining (pp. 11–52). Springer US DOI.
- Alain, G., & Bengio, Y. (2016) Understanding intermediate layers using linear classifier probes. arXiv:1610.01644 [Cs, Stat].
- Burrell, J. (2016) How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. DOI.
- Chipman, H. A., & Gu, H. (2005) Interpretable dimension reduction. Journal of Applied Statistics, 32(9), 969–987. DOI.
- 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.
- 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.
- 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).