The Living Thing / Notebooks :

Model interpretation, fairness and trust

The meeting point of differential privacy, accountability, interpretability, the tank detection story, clever horses in machine learning especially and the pertinent modern connection to working out if models are treating humans fairly, if fairness was not a criterion in training the models.

To put it another way, here are really two related problems:

  1. How can I work out what my model is using to tell me what it just told me?
  2. How can I ensure that my model is “fair” in what it does use?

Understanding black box models

Much work here; I understand little of it at the moment, but I keep needing to refer to papers here.

Think pieces on unfair models in practice


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].
Barocas, S., & Selbst, A. D.(2016) Big Data’s Disparate Impact (SSRN Scholarly Paper No. ID 2477899). . Rochester, NY: Social Science Research Network
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.
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).