Main Content
“Motivating the Rules of the Game for Adversarial Example Research” by Justin Gilmer et al., ArXiv (2018).
2.3.1
https://arxiv.org/pdf/1807.06732.pdf
[OPTIONAL] “Algorithmic Transparency for the Smart City” by Robert Brauneis & Ellen P. Goodman, Yale Journal of Law and Technology (2018)
3.2.2
https://yjolt.org/sites/default/files/20_yale_j._l._tech._103.pdf
[OPTIONAL] “Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment” by Joichi Ito, Jonathan Zittrain, et al. Proceedings of Fat*, (2108)
4.2.2
https://arxiv.org/pdf/1712.08238.pdf
[OPTIONAL] "The scored society: due process for automated predictions" by Danielle Keats Citron and Frank Pasquale, Washington Law Review (2014)
2.1.2
https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1318/89WLR0001.pdf
[OPTIONAL] “Towards a rigorous Science of Interpretable Machine Learning” by Finale Doshi-Velez and Been Kim, ArXiv (2017)
2.2.2
https://arxiv.org/pdf/1702.08608.pdf
[RECOMMENDED] “Intriguing properties of neural networks” by Christian Szegedy et al., ArXiv (2013)
2.3.2
https://arxiv.org/abs/1312.6199
“The Mythos of Model Interpretability” by Zachary C. Lipton, ArXiv (2016)
2.2.1
https://perma.cc/6MV7-HLHB
“Troubling Trends in Machine Learning Scholarship” by Zachary C. Lipton & Jacob Steinhardt (July 2018)
1.2
https://perma.cc/H6Q9-HZZD
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