Main Content
“Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru (Conference of Fairness, Accountability and Transparency, 2018)
2.1
http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
“Hidden Technical Debt in Machine Learning Systems” by D. Sculley et al., (NIPS, 2015)
5.3
https://perma.cc/PP9V-N4E6
“IBM, Microsoft and Amazon Not Letting Police Use Their Facial Recognition Technology” by Larry Magid (Forbes, 2020).
2.5
https://www.forbes.com/sites/larrymagid/2020/06/12/ibm-microsoft-and-amazon-not-letting-police-use-their-facial-recognition-technology/#14eadb611887
“Inherent Trade-Offs in the Fair Determination of Risk Scores” by Jon Kleinberg, Sendhil Mullainathan, et al. (ArXiv, 2016) - Read Introduction (p.1-3), Section 1.3, and Conclusion
3.5
https://perma.cc/NDT6-CTEZ
“Law and Adversarial Machine Learning” by Ram Shankar Siva Kumar et al. (ArXiv, 2018)
5.6
https://perma.cc/W46R-2GL7
"Machine Bias" by Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner (ProPublica, 2016)
3.1
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
“Machine Learning: A Primer: an introduction for both technical and non-technical readers” by Lizzie Turner (Medium: Artificial Intelligence, 2018)
1.3
https://medium.com/@iamlizzieturner/lets-talk-about-machine-learning-ddca914e9dd1
“One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority” by Paul Mozur (The New York Times, 2019).
2.3
https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html
"Please Stop Doing 'Explainable' ML" by Cynthia Rudin (VIDEO)
5.5
https://www.youtube.com/watch?v=I0yrJz8uc5Q
“ProPublica Responds to Company’s Critique of Machine Bias Story” by Julia Angwin and Jeff Larson (ProPublica, 2016)
3.4
https://perma.cc/579Z-BYT4
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