Meeting time: Wednesday 1:30-2:20

Following is a list of possible readings for the weekly session (under construction). As we move through the quarter entries will become more precise, reflecting the material actually worked on and including the reading assignements for the week.

• On everyone's mind
  • Sheri Fink The Hardest Questions Doctors May Face: Who Will Be Saved? Who Won't?
  • Ezekiel J. Emanuel, Govind Persad, Ross Upshur, Beatriz Thome, Michael Parker, Aaron Glickman, Cathy Zhang, Connor Boyle, Maxwell Smith, and James P. Phillips (2020) Fair Allocation of Scarce Medical Resources in the Time of Covid-19
• Signs of trouble
  • Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai (2016) Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
  • Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ProPublica Machine Bias
  • Popejoy, Ritter, Crooks, Currey, Fullerton, Hindorff, Koenig, Ramos, Sorokin, Wand, Wrigh, Zou, Gignoux, Bonham, Plon, Bustamante; Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group (ADWG) The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics
• Anti discrimination laws
  • C. Munoz , M. Smith, DJ Patil (2016) Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights Executive Office of the President
  • Civil rights act 1964 and US equal employment opportunity commission
• Contemporary ethics on justice and equality
  • Justice , Stanford Encyclopedia of Philosophy.
  • John Rawls Justice as Fairness: Political not Metaphysical
  • Elizabeth S. Anderson What Is the Point of Equality?
• The use of protected attributes in medicine
  • Risch, Burchard, Ziv, Tang (2002) Categorization of humans in biomedical research: genes, race and disease
  • Regression towards the mean, historically considered by Stephen Stigler
  • M. Stefanick (2017) Not just for men -- see also here
• Communicating the results of statistical analysis
  • Gould The real error of Cyril Burt from The mismeasure of men.
  • Donna L. Maney (2016) Perils and pitfalls of reporting sex differences
• Introductions definitions of fairness in Machine learning and Statistical Parity.
  • Alexandra Chouldechova, Aaron Roth (2018) The Frontiers of Fairness in Machine Learning
  • Alexandra Chouldechova (2016) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
• Individual fairness.
  • Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich Zemel (2011) Fairness Through Awareness
  • Michael P. Kim, Omer Reingold, Guy N. Rothblum Fairness Through Computationally-Bounded Awareness
• Equal opportunity and equalized odds
  • Moritz Hardt, Eric Price, Nathan Srebro Equality of Opportunity in Supervised Learning
  • Solon Barocas, Moritz Hardt, Arvind Narayanan Fairness in classification problems
• Causal fairness
  • Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva Causal Reasoning for Algorithmic Fairness
• Detecting and putting remedy to unfairness
  • E. Pierson, S Corbett-Davis, S. Goel (2018) Fast Threshold Tests for Detecting Discrimination
  • Dana Pessach, Erez Shmueli (2020) Algorithmic Fairness
  • Aequitas: A Bias and Fairness Audit Toolkit
• Other recent contributions
  • Corbett and Goel (2018) The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
  • Y. Romano R. F. Barber C. Sabatti E. J. Candes (2019) With malice towards none: Assessing uncertainty via equalized coverage.