In the second part of this series on Algorithmic Bias and Fairness, we look at how we can make artificial intelligence and algorithms fairer. To learn more about the math and statistics behind bias, visit http://brilliant.org/jordan and sign up for free. The first 200 people to sign up will also receive a 20% discount on the annual Premium subscription.

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Sources:
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Hardt, M., Price, E., & Srebro, N. (2016). Equal opportunities in supervised learning.

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

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

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

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Kasy, M., & Abebe, R. (nd). Fairness, Equality, and Power in Algorithmic Decision Making, 1–14.

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Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social data: biases, methodological pitfalls, and ethical boundaries. https://doi.org/10.3389/fdata.2019.00013

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

Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020). Saving Face: Exploring the Ethical Concerns of Facial Recognition Auditing. https://doi.org/10.1145/3375627.3375820

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

Some interesting Twitter threads that include these resources and more:
https://twitter.com/rajiinio/status/1275056558091747333
https://twitter.com/rctatman/status/1275183674007277569?s20
https://twitter.com/rajiinio/status/1275303539896651783?s20
https://twitter.com/maxkasy/status/1270024467268452354?s20

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