A repository of resources for Fairness in Machine Learning.

Core Concepts in Fairness

Sampling for Fairness

Encryption for Fairness

Causal Inference in Fairness

Other Code Repositories

References

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  1. Hashimoto, T. B., Srivastava, M., Namkoong, H., & Liang, P. (2018). Fairness without demographics in repeated loss minimization. International Conference on Machine Learning (ICML).
  2. Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2018). Delayed impact of fair machine learning. ArXiv:1803.04383.
  3. Kallus, N., & Zhou, A. (2018). Residual unfairness in fair machine learning from prejudiced data. ArXiv:1806.02887.
  4. Agarwal, A., Beygelzimer, A., Dudı́k Miroslav, Langford, J., & Wallach, H. (2018). A reductions approach to fair classification. ArXiv:1803.02453.
  5. Komiyama, J., Takeda, A., Honda, J., & Shimao, H. (2018). Nonconvex optimization for regression with fairness constraints. In International Conference on Machine Learning (ICML).
  6. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A Survey on Bias and Fairness in Machine Learning. ArXiv:1908.09635.
  7. Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. ArXiv:1808.00023.
  8. Celis, L. E., Keswani, V., Straszak, D., Deshpande, A., Kathuria, T., & Vishnoi, N. K. (2018). Fair and diverse DPP-based data summarization. ArXiv Preprint ArXiv:1802.04023.
  9. Amini, A., Soleimany, A., Schwarting, W., Bhatia, S., & Rus, D. (2019). Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure.
  10. Kilbertus, N., Gascón, A., Kusner, M. J., Veale, M., Gummadi, K. P., & Weller, A. (2018). Blind justice: Fairness with encrypted sensitive attributes. ArXiv:1806.03281.
  11. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. In Advances in Neural Information Processing Systems (NeurIPS).
  12. Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017). Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems (pp. 656–666).
  13. Zhang, J., & Bareinboim, E. (2018). Fairness in decision-making—the causal explanation formula. In AAAI Conference on Artificial Intelligence.
  14. Loftus, J. R., Russell, C., Kusner, M. J., & Silva, R. (2018). Causal reasoning for algorithmic fairness. ArXiv:1805.05859.
  15. Kusner, M. J., Russell, C., Loftus, J. R., & Silva, R. (2018). Causal Interventions for Fairness. ArXiv:1806.02380.
  16. Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2019). Fairness through causal awareness: Learning causal latent-variable models for biased data. In Conference on Fairness, Accountability, and Transparency.

A repository of resources for privacy constraints in computer vision.

Papers

References

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  1. Pittaluga, F., & Koppal, S. J. (2015). Privacy preserving optics for miniature vision sensors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  2. Pittaluga, F., & Koppal, S. J. (2016). Pre-capture privacy for small vision sensors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2215–2226.
  3. Pittaluga, F., Zivkovic, A., & Koppal, S. J. (2016). Sensor-level privacy for thermal cameras. In IEEE International Conference on Computational Photography (ICCP).
  4. Yonetani, R., Boddeti, V., Kitani, K. M., & Sato, Y. (2017). Privacy-preserving visual learning using doubly permuted homomorphic encryption. In IEEE International Conference on Computer Vision (ICCV).
  5. Boddeti, V. N. (2018). Secure face matching using fully homomorphic encryption. In IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS).
  6. Pittaluga, F., Koppal, S. J., Kang, S. B., & Sinha, S. N. (2019). Revealing scenes by inverting structure from motion reconstructions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  7. Pittaluga, F., Koppal, S., & Chakrabarti, A. (2019). Learning privacy preserving encodings through adversarial training. In IEEE Winter Conference on Applications of Computer Vision (WACV).
  8. Wang, Z. W., Vineet, V., Pittaluga, F., Sinha, S. N., Cossairt, O., & Bing Kang, S. (2019). Privacy-Preserving Action Recognition using Coded Aperture Videos. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
  9. Speciale, P., Schonberger, J. L., Kang, S. B., Sinha, S. N., & Pollefeys, M. (2019). Privacy preserving image-based localization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  10. Speciale, P., Schönberger, J. L., Sinha, S. N., & Pollefeys, M. (2019). Privacy Preserving Image Queries for Camera Localization. In IEEE International Conference on Computer Vision (ICCV).

A repository of resources for Representation Learning as applicable to invariance, fairness or information leakage.

Papers

Other Code Repositories

References

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  1. Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013). Learning fair representations. In International Conference on Machine Learning (ICML).
  2. Edwards, H., & Storkey, A. (2015). Censoring representations with an adversary. ArXiv:1511.05897.
  3. Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2015). The variational fair autoencoder. ArXiv:1511.00830.
  4. Xie, Q., Dai, Z., Du, Y., Hovy, E., & Neubig, G. (2017). Controllable invariance through adversarial feature learning. In Advances in Neural Information Processing Systems (NeurIPS).
  5. Pérez-Suay, A., Laparra, V., Mateo-Garcı́a Gonzalo, Muñoz-Marı́ Jordi, Gómez-Chova, L., & Camps-Valls, G. (2017). Fair kernel learning. In European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD).
  6. Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2018). Learning adversarially fair and transferable representations. ArXiv:1802.06309.
  7. Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., & Ver Steeg, G. (2018). Invariant Representations without Adversarial Training. In Advances in Neural Information Processing Systems (NeurIPS).
  8. Madras, D., Pitassi, T., & Zemel, R. (2018). Predict responsibly: improving fairness and accuracy by learning to defer. In Advances in Neural Information Processing Systems (NeurIPS).
  9. Elazar, Y., & Goldberg, Y. (2018). Adversarial removal of demographic attributes from text data. In Empirical Methods in Natural Language Processing (EMNLP).
  10. Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Mitigating unwanted biases with adversarial learning. In Conference on Artificial Intelligence, Ethics and Soceity (AIES).
  11. Roy, P., & Boddeti, V. (2019). Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  12. Song, J., Kalluri, P., Grover, A., Zhao, S., & Ermon, S. (2019). Learning Controllable Fair Representations. In International Conference on Artificial Intelligence and Statistics (AISTATS).
  13. Sadeghi, B., Yu, R., & Boddeti, V. (2019). On the Global Optima of Kernelized Adversarial Representation Learning. In International Conference on Computer Vision (ICCV).
  14. Bertran, M., Martinez, N., Papadaki, A., Qiu, Q., Rodrigues, M., Reeves, G., & Sapiro, G. (2019). Adversarially Learned Representations for Information Obfuscation and Inference. In International Conference on Machine Learning (ICML).
  15. Tan, Z., Yeom, S., Fredrikson, M., & Talwalkar, A. (2019). Learning Fair Representations for Kernel Models. ArXiv:1906.11813.
  16. Creager, E., Madras, D., Jacobsen, J.-H., Weis, M. A., Swersky, K., Pitassi, T., & Zemel, R. (2019). Flexibly Fair Representation Learning by Disentanglement. In International Conference on Machine Learning (ICML).
  17. Kim, B., Kim, H., Kim, K., Kim, S., & Kim, J. (2019). Learning Not to Learn: Training Deep Neural Networks with Biased Data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  18. Sarafianos, N., Xu, X., & Kakadiaris, I. A. (2019). Adversarial Representation Learning for Text-to-Image Matching. In International Conference on Computer Vision (ICCV).
  19. Moyer, D., Steeg, G. V., Tax, C. M. W., & Thompson, P. M. (2019). Scanner Invariant Representations for Diffusion MRI Harmonization. ArXiv Preprint ArXiv:1904.05375.
  20. Jaiswal, A., Moyer, D., Steeg, G. V., AbdAlmageed, W., & Natarajan, P. (2020). Invariant Representations through Adversarial Forgetting. In AAAI Conference on Artificial Intelligence (AAAI).