How To Make Algorithms Fairer | Algorithmic Bias and Fairness

In the second part of this series on Algorithmic Bias and Fairness, we're looking at how we can make artificial intelligence and algorithms fairer. If you're interested in learning about the math and statistics behind bias, go to brilliant.org/jordan and sign up for free. The first 200 people to sign up will also get 20% off the annual Premium subscription.

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Sources:
Bender, E. M., & Friedman, B. (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. doi.org/10.1162/tacl_a_00041

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Debiasing Word Embedding. Retrieved from code.google.com/archive/p/word2vec/

Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. doi.org/10.1089/big.2016.0047

DeVries, T., Misra, I., W*ng, C., & van der Maaten, L. (2019). Does Object Recognition Work for Everyone? Retrieved from arxiv.org/abs/1906.02659

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018). Datasheets for Datasets. Retrieved from arxiv.org/abs/1803.09010

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks.

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning.

Hoffmann, A. L. (2019). Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. doi.org/10.1080/1369118X.2019.1573912

Hovy, D., & Spruit, S. L. (2016). The social impact of natural language processing.

Hu, L., & Chen, Y. (2020). Fair classification and social welfare. doi.org/10.1145/3351095.3372857

Jia, S., Meng, T., Zhao, J., & Chang, K.-W. (2020). Mitigating Gender Bias Amplification in Distribution by Posterior Regularization. Retrieved from arxiv.org/abs/2005.06251

Jo, E. S., & Gebru, T. (2020). Lessons from archives: Strategies for collecting sociocultural data in machine learning. doi.org/10.1145/3351095.3372829

Kasy, M., & Abebe, R. (n.d.). Fairness, equality, and power in algorithmic decisionmaking, 1–14.

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. doi.org/10.4230/LIPIcs.ITCS.2017.43

Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi.org/10.1038/nature14539

Mitchell, S., Potash, E., Barocas, S., D’Amour, A., & Lum, K. (2018). Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions, 1–22. Retrieved from arxiv.org/abs/1811.07867

Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. doi.org/10.3389/fdata.2019.00013

Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. doi.org/10.1145/3306618.3314244

Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020). Saving Face: Investigating the ethical concerns of facial recognition auditing. doi.org/10.1145/3375627.3375820

Shankar, S., Halpern, Y., Breck, E., Atwood, J., Wilson, J., & Sculley, D. (2017). No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World, Retrieved from arxiv.org/abs/1711.08536

Stock, P., & Cisse, M. (2018). ConvNets and imagenet beyond accuracy: Understanding mistakes and uncovering biases. 11210 LNCS, 504–519. doi.org/10.1007/978-3-030-01231-1_31

Suresh, H., & Guttag, J. V. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. Retrieved from arxiv.org/abs/1901.10002

Verma, S., & Rubin, J. (2018). Fairness definitions explained. Proceedings - International Conference on Software Engineering, 1–7. doi.org/10.1145/3194770.3194776

W*ng, T, Zhao, J., Yatskar, M., Chang, K. W., & Ordonez, V. (2019). Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. doi.org/10.1109/ICCV.2019.00541

Wilson, B., Hoffman, J., & Morgenstern, J. (2019). Predictive Inequity in Object Detection. Retrieved from arxiv.org/abs/1902.11097

Zhao, J., W*ng, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2017). Men also like shopping: Reducing gender bias amplification using corpus-level constraints. doi.org/10.18653/v1/d17-1323

Some interesting Twitter threads containing these resources and more:
twitter.com/rajiinio/status/1275056558091747333
twitter.com/rctatman/status/1275183674007277569…
twitter.com/rajiinio/status/1275303539896651783…
twitter.com/maxkasy/status/1270024467268452354?…