The goal of the course is to explore quantitative approaches to
fairness, especially issues related to recent applications of
algorithmic techniques to determine social and human related
decisions. As background material we will cover (cooperative) Game
Theory, Cryptography, Differential Privacy (previous semester course)
Machine Learning and then consider the issues in various settings
Battling Algorithmic Bias By Keith Kirkpatrick, CACM 2016 No. 10,
Pages 16-17 https://cacm.acm.org/magazines/2016/10/207759-battling-algorithmic-bias/fulltext
DP Style:
* Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich
Zemel, Fairness Through Awareness
* Richard Zemel, Yu (Ledell) Wu, Kevin Swersky, Toniann Pitassi,
Cynthia Dwork, Learning Fair Representation,
https://www.cs.toronto.edu/~toni/Papers/icml-final.pdf
* Shokri and Shamtikov, Membership Inference Attacks Against Machine
Learning Models http://www.cs.cornell.edu/~shmat/shmat_oak17.pdf
DISCRIMINATION
* Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan, Inherent
Trade-offs in the Fair Determination of Risk Scores
https://arxiv.org/abs/1609.05807
* Alexandra Chouldechova, Fair prediction with disparate impact: A
study of bias in recidivism prediction instruments,
https://arxiv.org/abs/1703.00056
* Ke Yang, Julia Stoyanovich, Measuring Fairness in Ranked Outputs
https://arxiv.org/abs/1610.08559
Bias in learning based on DB:
* Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kala
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
Embeddings, https://arxiv.org/abs/1607.06520
* Aylin Caliskan-Islam , Joanna J. Bryson and Arvind Narayanan,
Semantics derived automatically from language corpora necessarily
contain human biases
http://randomwalker.info/publications/language-bias.pdf
Interpretable ML
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan,
Interpretable classifiers using rules and Bayesian analysis: Building
a better stroke prediction model
CRYPTO RELATED
MPC and Fairness
*Cleve, Gordon HKL
* Bitcoin and blockchain
* Accoutable Algorithms, Kroll and Felten
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2765268
https://www.youtube.com/watch?v=mTNV0QI1cg4
Papers to criticize:
* http://www.pnas.org/content/110/15/5802.full
Courses: Suresh Venkat https://geomblog.github.io/fairness/
Moritz Hardt https://fairmlclass.github.io/