A snapshot of the frontiers of fairness in machine learning

A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

[1]  Aaron Roth,et al.  Fairness in Learning: Classic and Contextual Bandits , 2016, NIPS.

[2]  Adam Tauman Kalai,et al.  Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.

[3]  K. Lum,et al.  To predict and serve? , 2016 .

[4]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[5]  Julia Stoyanovich,et al.  Measuring Fairness in Ranked Outputs , 2016, SSDBM.

[6]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[7]  Cheng Soon Ong,et al.  Provably Fair Representations , 2017, ArXiv.

[8]  Toon Calders,et al.  Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.

[9]  Khashayar Khosravi,et al.  Exploiting the Natural Exploration In Contextual Bandits , 2017, ArXiv.

[10]  Jon M. Kleinberg,et al.  Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.

[11]  Suresh Venkatasubramanian,et al.  On the (im)possibility of fairness , 2016, ArXiv.

[12]  Latanya Sweeney,et al.  Discrimination in online ad delivery , 2013, CACM.

[13]  Bernhard Schölkopf,et al.  Avoiding Discrimination through Causal Reasoning , 2017, NIPS.

[14]  Aaron Roth,et al.  Meritocratic Fairness for Cross-Population Selection , 2017, ICML.

[15]  Cynthia Dwork,et al.  Fairness Under Composition , 2018, ITCS.

[16]  Kristian Lum,et al.  An algorithm for removing sensitive information: Application to race-independent recidivism prediction , 2017, The Annals of Applied Statistics.

[17]  Toniann Pitassi,et al.  Learning Fair Representations , 2013, ICML.

[18]  Amos J. Storkey,et al.  Censoring Representations with an Adversary , 2015, ICLR.

[19]  Suresh Venkatasubramanian,et al.  Runaway Feedback Loops in Predictive Policing , 2017, FAT.

[20]  Chiara Sabatti,et al.  MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS. , 2017, The annals of applied statistics.

[21]  Jon M. Kleinberg,et al.  Selection Problems in the Presence of Implicit Bias , 2018, ITCS.

[22]  Franco Turini,et al.  Discrimination-aware data mining , 2008, KDD.

[23]  Philip S. Thomas,et al.  Importance Sampling for Fair Policy Selection , 2017, UAI.

[24]  Seth Neel,et al.  An Empirical Study of Rich Subgroup Fairness for Machine Learning , 2018, FAT.

[25]  Fernando Diaz,et al.  Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI , 2016 .

[26]  Alexandra Chouldechova,et al.  Fair prediction with disparate impact: A study of bias in recidivism prediction instruments , 2016, Big Data.

[27]  Ilya Shpitser,et al.  Fair Inference on Outcomes , 2017, AAAI.

[28]  Nisheeth K. Vishnoi,et al.  Fair Personalization , 2017, ArXiv.

[29]  Esther Rolf,et al.  Delayed Impact of Fair Machine Learning , 2018, ICML.

[30]  Guy N. Rothblum,et al.  Calibration for the (Computationally-Identifiable) Masses , 2017, ArXiv.

[31]  Timnit Gebru,et al.  Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.

[32]  Zhe Zhao,et al.  Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.

[33]  Jun Sakuma,et al.  Fairness-aware Learning through Regularization Approach , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[34]  David Sontag,et al.  Why Is My Classifier Discriminatory? , 2018, NeurIPS.

[35]  Sampath Kannan,et al.  Downstream Effects of Affirmative Action , 2018, FAT.

[36]  Seth Neel,et al.  Meritocratic Fairness for Infinite and Contextual Bandits , 2018, AIES.

[37]  Blake Lemoine,et al.  Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.

[38]  Stephen Coate,et al.  Will Affirmative-Action Policies Eliminate Negative Stereotypes? , 1993 .

[39]  Guy N. Rothblum,et al.  Probably Approximately Metric-Fair Learning , 2018, ICML.

[40]  Christopher Jung,et al.  Online Learning with an Unknown Fairness Metric , 2018, NeurIPS.

[41]  Krishna P. Gummadi,et al.  Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.

[42]  Toniann Pitassi,et al.  Predict Responsibly: Increasing Fairness by Learning To Defer , 2018, ICLR.

[43]  James Y. Zou,et al.  Multiaccuracy: Black-Box Post-Processing for Fairness in Classification , 2018, AIES.

[44]  Nathan Srebro,et al.  Equality of Opportunity in Supervised Learning , 2016, NIPS.

[45]  Carlos Eduardo Scheidegger,et al.  Certifying and Removing Disparate Impact , 2014, KDD.

[46]  Yiling Chen,et al.  A Short-term Intervention for Long-term Fairness in the Labor Market , 2017, WWW.

[47]  John Langford,et al.  A Reductions Approach to Fair Classification , 2018, ICML.

[48]  Toniann Pitassi,et al.  Learning Adversarially Fair and Transferable Representations , 2018, ICML.

[49]  M. Kearns,et al.  Fairness in Criminal Justice Risk Assessments: The State of the Art , 2017, Sociological Methods & Research.

[50]  Seth Neel,et al.  Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness , 2017, ICML.

[51]  Dean P. Foster,et al.  An Economic Argument for Affirmative Action , 1992 .

[52]  Guy N. Rothblum,et al.  Fairness Through Computationally-Bounded Awareness , 2018, NeurIPS.

[53]  Seth Neel,et al.  Fair Algorithms for Infinite and Contextual Bandits , 2016, 1610.09559.

[54]  Avi Feller,et al.  Algorithmic Decision Making and the Cost of Fairness , 2017, KDD.

[55]  Yang Liu,et al.  Calibrated Fairness in Bandits , 2017, ArXiv.

[56]  Sampath Kannan,et al.  A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem , 2018, NeurIPS.

[57]  Sebastian Ehrlichmann,et al.  The Economics of Discrimination , 2009 .

[58]  Zhiwei Steven Wu,et al.  The Unfair Externalities of Exploration , 2017 .

[59]  Toniann Pitassi,et al.  Fairness through awareness , 2011, ITCS '12.

[60]  Nathan Srebro,et al.  Learning Non-Discriminatory Predictors , 2017, COLT.

[61]  Sampath Kannan,et al.  Fairness Incentives for Myopic Agents , 2017, EC.

[62]  Arvind Narayanan,et al.  Semantics derived automatically from language corpora contain human-like biases , 2016, Science.

[63]  Aaron Roth,et al.  Fairness in Reinforcement Learning , 2016, ICML.

[64]  Nisheeth K. Vishnoi,et al.  Ranking with Fairness Constraints , 2017, ICALP.