公平机器学习:概念、分析与设计

[21] Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries[J] . Olteanu Alexandra,Castillo Carlos,Diaz Fernando,K?c?man Emre.Frontiers in Big Data . 2019

[22] Establishing the rules for building trustworthy AI [J]. NATURE MACHINE INTELLIGENCE, 2019, 1 (06) : 261 - 262

[23] Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads [J]. MANAGEMENT SCIENCE, 2019, 65 (07) : 2966 - 2981

[24] Assessing gender bias in machine translation: a case study with Google Translate[J] . Marcelo O. R. Prates,Pedro H. Avelar,Luís C. Lamb.Neural Computing and Applications . 2019 (prep)

[25] AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias[J] . Rachel K. E. Bellamy,Kuntal Dey,Michael Hind,Samuel C. Hoffman,Stephanie Houde,Kalapriya Kannan,Pranay Lohia,Jacquelyn Martino,Sameep Mehta,Aleksandra Mojsilovic,Seema Nagar,Karthikeyan Natesan Ramamurthy,John T. Richards,Diptikalyan Saha,Prasanna Sattigeri,Moninder Singh,Kush R. Varshney,Yunfeng Zhang.IBM Journal of Research and Development . 2019 (4/5)

[26] A Framework for Understanding Unintended Consequences of Machine Learning[J] . Harini Suresh,John V. Guttag.CoRR . 2019

[27] Fairness with Dynamics[J] . Min Wen,Osbert Bastani,Ufuk Topcu.CoRR . 2019

[28] Deep Learning for Face Recognition: Pride or Prejudiced?[J] . Shruti Nagpal,Maneet Singh,Richa Singh 0001,Mayank Vatsa,Nalini K. Ratha.CoRR . 2019

[29] Fair Meta-Learning: Learning How to Learn Fairly[J] . Dylan Slack,Sorelle A. Friedler,Emile Givental.CoRR . 2019

[30] Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning[J] . Mohammad Yaghini,Bogdan Kulynych,Carmela Troncoso.CoRR . 2019

[21] Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries[J] . Olteanu Alexandra,Castillo Carlos,Diaz Fernando,K?c?man Emre.Frontiers in Big Data . 2019

[22] Establishing the rules for building trustworthy AI [J]. NATURE MACHINE INTELLIGENCE, 2019, 1 (06) : 261 - 262

[23] Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads [J]. MANAGEMENT SCIENCE, 2019, 65 (07) : 2966 - 2981

[24] Assessing gender bias in machine translation: a case study with Google Translate[J] . Marcelo O. R. Prates,Pedro H. Avelar,Luís C. Lamb.Neural Computing and Applications . 2019 (prep)

[25] AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias[J] . Rachel K. E. Bellamy,Kuntal Dey,Michael Hind,Samuel C. Hoffman,Stephanie Houde,Kalapriya Kannan,Pranay Lohia,Jacquelyn Martino,Sameep Mehta,Aleksandra Mojsilovic,Seema Nagar,Karthikeyan Natesan Ramamurthy,John T. Richards,Diptikalyan Saha,Prasanna Sattigeri,Moninder Singh,Kush R. Varshney,Yunfeng Zhang.IBM Journal of Research and Development . 2019 (4/5)

[26] A Framework for Understanding Unintended Consequences of Machine Learning[J] . Harini Suresh,John V. Guttag.CoRR . 2019

[27] Fairness with Dynamics[J] . Min Wen,Osbert Bastani,Ufuk Topcu.CoRR . 2019

[28] Deep Learning for Face Recognition: Pride or Prejudiced?[J] . Shruti Nagpal,Maneet Singh,Richa Singh 0001,Mayank Vatsa,Nalini K. Ratha.CoRR . 2019

[29] Fair Meta-Learning: Learning How to Learn Fairly[J] . Dylan Slack,Sorelle A. Friedler,Emile Givental.CoRR . 2019

[30] Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning[J] . Mohammad Yaghini,Bogdan Kulynych,Carmela Troncoso.CoRR . 2019