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Tuesday, January 16

  1. msg possible paper message posted possible paper Analyzing fairness in election results based on order of names on ballot BallotMaps: Detecting Nam…
    possible paper
    Analyzing fairness in election results based on order of names on ballot
    BallotMaps: Detecting Name Bias in Alphabetically Ordered Ballot Papers
    http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6065005

    -wu

Thursday, January 11

  1. 8:12 am

Monday, January 8

  1. page fairness edited Fairness in Data Science and Machine Learning: a reading list MEDIA ARTICLES and CASE STUDIES …

    Fairness in Data Science and Machine Learning: a reading list

    MEDIA ARTICLES and CASE STUDIES
    Recidivism prediction
    (view changes)
    6:43 pm
  2. page fairness edited Reading list MEDIA ARTICLES and CASE STUDIES Recidivism prediction
    Reading list
    MEDIA ARTICLES and CASE STUDIES
    Recidivism prediction
    (view changes)
    6:41 pm
  3. page fairness edited ... Learning Fair Representation, Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork, I…
    ...
    Learning Fair Representation, Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork, ICML 2013
    Optimized Pre-Processing for Discrimination Prevention, Flavio P. Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, NIPS 2017
    ...
    causal modeling
    Counterfactual Fairness, M J Kusner, J R Loftus, C Russell, and R Silva, NIPS 2017
    Avoiding Discrimination through Causal Reasoning, Kilbertus, Niki, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. NIPS 17
    ...
    Suresh Venkatasubramanian’s class: https://geomblog.github.io/fairness/
    Tutorial at NIPS 2017: http://mrtz.org/nips17/#/

    (view changes)
    6:40 pm
  4. page fairness edited Reading list MEDIA ARTICLES and CASE STUDIES Recidivism prediction Risk as a Proxy for Race, Cr…
    Reading list
    MEDIA ARTICLES and CASE STUDIES
    Recidivism prediction
    Risk as a Proxy for Race, Criminology and Public Policy, Forthcoming. Bernard E. Harcourt.
    Media articles on bias in COMPASsoftware
    https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing/
    https://www.propublica.org/article/bias-in-criminal-risk-scores-is-mathematically-inevitable-researchers-say
    Some follow-up technical articles
    Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, Alexandra Chouldechova. FATML 2016 (also published in Big Data Journal)
    Jennifer L. Skeem and Christopher T. Lowenkamp. Risk, race, and recidivism: Predictive bias and disparate impact. 2016.
    Discriminatory Ad targeting
    Media articles
    Facebook, Amazon, T-Mobile, others sued over alleged age discrimination in Facebook job ads, Levi Sumagaysay and Queenie Wong, Mercury News, Dec 21, 2017
    Facebook (Still) Letting Housing Advertisers Exclude Users by Race, Julia Angwin, Ariana Tobin and Madeleine Varner Nov. 21, 2017, Propublica
    Facebook has discriminated against you, and it's not going to stop, Nov 12, 2016, Mashable.
    Women less likely to be shown ads for high-paid jobs on Google, study shows, Samuel Gibbs, 8 July 2015, The Guardian
    Case study (referred to in some of the above media articles)
    Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination, Amit Datta, Michael Carl Tschantz, Anupam Datta, CMU
    Law enforcement
    Precinct or prejudice? Understanding racial disparities in New York City's stop-and-frisk policy Sharad Goel, Justin M. Rao and Ravi Shroff
    A large-scale analysis of racial disparities in police stops across the United States, Pierson, Emma, Camelia Simoiu, Jan Overgoor, Sam Corbett-Davies, Vignesh Ramachandran, Cheryl Phillips, and Sharad Goel (2017).
    College admissions
    Sex Bias in Graduate Admissions: Data from Berkeley Author(s): P. J. Bickel, E. A. Hammel, J. W. O'Connell
    Differential pricing
    Websites Vary Prices, Deals Based on Users' Information, Jennifer Valentino-DeVries, Jeremy Singer-Vine and Ashkan Soltani
    Measuring Price Discrimination and Steering on E-commerce Web Sites, Aniko Hannak, Gary Soeller, David Lazer, Alan Mislove, Christo Wilson, IMC 2014
    Big data and differential pricing, White house report, Feb 2015.
    DISPARATE IMPACT in PREDICTION/CLASSIFICATION
    Definitions, impossibility and tradeoffs
    Inherent Trade-Offs in the Fair Determination of Risk Scores, Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan. ITCS 2017.
    On fairness and calibration, Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger, NIPS 2017
    “[...] we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates.”
    Equality of Opportunity in Supervised Learning, Moritz Hardt, Eric Price, Nathan Srebro NIPS 2016.
    This is one of the papers referenced in the 2nd ProPublica article.
    Certifying and removing disparate impact, Michael Feldman, Sorelle Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian, KDD 2015
    Fairness in Criminal Justice Risk Assessments: The State of the Art, Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth, 2017
    “In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. “
    Algorithmic Decision Making and the Cost of Fairness, Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, Aziz Huq. KDD 2017
    On Fairness, Diversity and Randomness in Algorithmic Decision Making, Muhammad Bilal Zafar, Krishna Gummadi, and Adrian Weller, FATML 2017
    “We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning…”
    Fairness through data representation/preprocessing
    Fairness Through Awareness Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich Zemel,ITCS 2012
    Learning Fair Representation, Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork, ICML 2013
    Optimized Pre-Processing for Discrimination Prevention, Flavio P. Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, NIPS 2017
    Fairness through causal modeling
    Counterfactual Fairness, M J Kusner, J R Loftus, C Russell, and R Silva, NIPS 2017
    Avoiding Discrimination through Causal Reasoning, Kilbertus, Niki, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. NIPS 17
    OTHER TOPICS IN FAIRNESS
    Beyond parity
    Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment, Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi, WWW 2017
    Fairness Beyond Non-discrimination: Feature Selection for Fair Decision Making, N Grgic-Hlaca, M B Zafar, K P Gummadi, and A Weller, KDD 2017
    From Parity to Preference-based Notions of Fairness in Classification, Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna Gummadi, and Adrian Weller, FATML 2017
    Beyond Parity: Fairness Objectives for Collaborative Filtering. NIPS 2017
    Systems/Practice
    Achieving non-discrimination in data release, Lu Zhang, Yongkai Wu, and Xintao Wu
    Fairness Testing: Testing Software for Discrimination, Sainyam Galhotra, Yuriy Brun, Alexandra Meliou
    Meritocratic fairness
    “Algorithms should never at any round place higher selection probability on a less qualified applicant than on a more qualified applicant”
    Fairness in Reinforcement Learning Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth
    Fairness in Learning: Classic and Contextual Bandits, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth, NIPS 2016
    Meritocratic Fairness for Cross-Population Selection, Michael Kearns, A Roth, and Z S Wu, ICML 2017
    Fair Allocation
    On the Efficiency-Fairness Trade-Off (with D. Bertsimas and V. F. Farias) (Management Science, Vol. 58, No. 12, 2012)
    Online Stochastic Ad Allocation: Efficiency and Fairness, Jon Feldman, Monika Henzinger, Nitish Korula, Vahab S. Mirrokni, Clifford Stein: ESA 2010
    Alex Sherman, Jason Nieh, Cliff Stein FairTorrent: A Deficit-Based Distributed Algorithm to Ensure Fairness in Peer-to-Peer Systems. IEEE/ACM Trans. Netw. 20(5): 1361-1374(2012)
    OTHER RESOURCES
    Moritz Hardt class at UC Berkeley: https://fairmlclass.github.io/
    Suresh Venkatasubramanian’s class: https://geomblog.github.io/fairness/
    Tutorial at NIPS 2017: http://mrtz.org/nips17/#/

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    6:37 pm
  5. page fairness edited Fairness reading Reading list
    Fairness readingReading list
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    6:35 pm
  6. page fairness edited Reading Fairness reading list
    ReadingFairness reading list
    (view changes)
    6:35 pm
  7. 6:34 pm
  8. page fairness edited Reading list
    Reading list
    (view changes)
    6:34 pm

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