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Inherent Trade-Offs in the Fair Determination of Risk Scores
v1v2 (latest)

Inherent Trade-Offs in the Fair Determination of Risk Scores

19 September 2016
Jon M. Kleinberg
S. Mullainathan
Manish Raghavan
    FaML
ArXiv (abs)PDFHTML

Papers citing "Inherent Trade-Offs in the Fair Determination of Risk Scores"

50 / 747 papers shown
Title
Understanding artificial intelligence ethics and safety
Understanding artificial intelligence ethics and safety
David Leslie
FaMLAI4TS
74
363
0
11 Jun 2019
Equalized odds postprocessing under imperfect group information
Equalized odds postprocessing under imperfect group information
Pranjal Awasthi
Matthäus Kleindessner
Jamie Morgenstern
92
91
0
07 Jun 2019
Fair Division Without Disparate Impact
Fair Division Without Disparate Impact
A. Peysakhovich
Christian Kroer
64
10
0
06 Jun 2019
Does Object Recognition Work for Everyone?
Does Object Recognition Work for Everyone?
Terrance Devries
Ishan Misra
Changhan Wang
Laurens van der Maaten
118
265
0
06 Jun 2019
Balanced Ranking with Diversity Constraints
Balanced Ranking with Diversity Constraints
Ke Yang
Vasilis Gkatzelis
Julia Stoyanovich
53
71
0
04 Jun 2019
Assessing Algorithmic Fairness with Unobserved Protected Class Using
  Data Combination
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus
Xiaojie Mao
Angela Zhou
FaML
110
158
0
01 Jun 2019
Metric Learning for Individual Fairness
Metric Learning for Individual Fairness
Christina Ilvento
FaML
111
97
0
01 Jun 2019
Achieving Fairness in Determining Medicaid Eligibility through Fairgroup
  Construction
Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
Boli Fang
Miao Jiang
Jerry Shen
26
2
0
01 Jun 2019
Optimized Score Transformation for Consistent Fair Classification
Optimized Score Transformation for Consistent Fair Classification
Dennis L. Wei
Karthikeyan N. Ramamurthy
Flavio du Pin Calmon
54
16
0
31 May 2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Alekh Agarwal
Miroslav Dudík
Zhiwei Steven Wu
FaML
88
249
0
30 May 2019
Efficient candidate screening under multiple tests and implications for
  fairness
Efficient candidate screening under multiple tests and implications for fairness
Lee Cohen
Zachary Chase Lipton
Yishay Mansour
68
32
0
27 May 2019
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Yixin Wang
Dhanya Sridhar
David M. Blei
FaML
70
20
0
26 May 2019
Field-aware Calibration: A Simple and Empirically Strong Method for
  Reliable Probabilistic Predictions
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
Feiyang Pan
Xiang Ao
Pingzhong Tang
Min Lu
Dapeng Liu
Lei Xiao
Qing He
72
22
0
26 May 2019
Average Individual Fairness: Algorithms, Generalization and Experiments
Average Individual Fairness: Algorithms, Generalization and Experiments
Michael Kearns
Aaron Roth
Saeed Sharifi-Malvajerdi
FaMLFedML
130
87
0
25 May 2019
Protecting the Protected Group: Circumventing Harmful Fairness
Protecting the Protected Group: Circumventing Harmful Fairness
Omer Ben-Porat
Fedor Sandomirskiy
Moshe Tennenholtz
FaML
73
18
0
25 May 2019
From What to How: An Initial Review of Publicly Available AI Ethics
  Tools, Methods and Research to Translate Principles into Practices
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
Jessica Morley
Luciano Floridi
Libby Kinsey
Anat Elhalal
83
57
0
15 May 2019
Proportionally Fair Clustering
Proportionally Fair Clustering
Xingyu Chen
Brandon Fain
Charles Lyu
Kamesh Munagala
FedMLFaML
123
144
0
09 May 2019
Fair Classification and Social Welfare
Fair Classification and Social Welfare
Lily Hu
Yiling Chen
FaML
90
92
0
01 May 2019
Fairness-Aware Ranking in Search & Recommendation Systems with
  Application to LinkedIn Talent Search
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
S. Geyik
Stuart Ambler
K. Kenthapadi
115
384
0
30 Apr 2019
Learning Fair Representations via an Adversarial Framework
Learning Fair Representations via an Adversarial Framework
Rui Feng
Yang Yang
Yuehan Lyu
Chenhao Tan
Yizhou Sun
Chunping Wang
FaML
83
56
0
30 Apr 2019
Tracking and Improving Information in the Service of Fairness
Tracking and Improving Information in the Service of Fairness
Sumegha Garg
Michael P. Kim
Omer Reingold
FaML
53
13
0
22 Apr 2019
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine
  Learning
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Ángel Alexander Cabrera
Will Epperson
Fred Hohman
Minsuk Kahng
Jamie Morgenstern
Duen Horng Chau
FaML
128
187
0
10 Apr 2019
Fairness in Algorithmic Decision Making: An Excursion Through the Lens
  of Causality
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
A. Khademi
Sanghack Lee
David Foley
Vasant Honavar
FaML
76
96
0
27 Mar 2019
The invisible power of fairness. How machine learning shapes democracy
The invisible power of fairness. How machine learning shapes democracy
E. Beretta
A. Santangelo
Bruno Lepri
A. Vetrò
Juan Carlos De Martin
FaML
44
7
0
22 Mar 2019
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
  Classification
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Daniel Borkan
Lucas Dixon
Jeffrey Scott Sorensen
Nithum Thain
Lucy Vasserman
101
493
0
11 Mar 2019
Fairness for Robust Log Loss Classification
Fairness for Robust Log Loss Classification
Ashkan Rezaei
Rizal Fathony
Omid Memarrast
Brian Ziebart
FaML
80
8
0
10 Mar 2019
On the Long-term Impact of Algorithmic Decision Policies: Effort
  Unfairness and Feature Segregation through Social Learning
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari
Vedant Nanda
Krishna P. Gummadi
67
67
0
04 Mar 2019
Fairness in Recommendation Ranking through Pairwise Comparisons
Fairness in Recommendation Ranking through Pairwise Comparisons
Alex Beutel
Jilin Chen
Tulsee Doshi
Hai Qian
Li Wei
...
Lukasz Heldt
Zhe Zhao
Lichan Hong
Ed H. Chi
Cristos Goodrow
FaML
116
381
0
02 Mar 2019
Predictive Inequity in Object Detection
Predictive Inequity in Object Detection
Benjamin Wilson
Judy Hoffman
Jamie Morgenstern
89
220
0
21 Feb 2019
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and
  the xAUC Metric
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Nathan Kallus
Angela Zhou
102
76
0
15 Feb 2019
Fair Decisions Despite Imperfect Predictions
Fair Decisions Despite Imperfect Predictions
Niki Kilbertus
Manuel Gomez Rodriguez
Bernhard Schölkopf
Krikamol Muandet
Isabel Valera
FaMLOffRL
78
5
0
08 Feb 2019
Equal Opportunity in Online Classification with Partial Feedback
Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod
Katrina Ligett
Aaron Roth
Bo Waggoner
Zhiwei Steven Wu
FaML
78
60
0
06 Feb 2019
Repairing without Retraining: Avoiding Disparate Impact with
  Counterfactual Distributions
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang
Berk Ustun
Flavio du Pin Calmon
FaML
123
85
0
29 Jan 2019
Fair Regression for Health Care Spending
Fair Regression for Health Care Spending
A. Zink
Sherri Rose
103
50
0
28 Jan 2019
Guarantees for Spectral Clustering with Fairness Constraints
Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner
Samira Samadi
Pranjal Awasthi
Jamie Morgenstern
108
159
0
24 Jan 2019
Identifying and Correcting Label Bias in Machine Learning
Identifying and Correcting Label Bias in Machine Learning
Heinrich Jiang
Ofir Nachum
FaML
104
285
0
15 Jan 2019
Fair and Unbiased Algorithmic Decision Making: Current State and Future
  Challenges
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Songül Tolan
FaML
38
31
0
15 Jan 2019
Putting Fairness Principles into Practice: Challenges, Metrics, and
  Improvements
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Alex Beutel
Jilin Chen
Tulsee Doshi
Hai Qian
Allison Woodruff
Christine Luu
Pierre Kreitmann
Jonathan Bischof
Ed H. Chi
FaML
108
153
0
14 Jan 2019
Fair Algorithms for Clustering
Fair Algorithms for Clustering
Suman Kalyan Bera
Deeparnab Chakrabarty
Nicolas J. Flores
Maryam Negahbani
FaMLFedML
79
242
0
08 Jan 2019
Impossibility and Uncertainty Theorems in AI Value Alignment (or why
  your AGI should not have a utility function)
Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)
P. Eckersley
119
46
0
31 Dec 2018
Improving fairness in machine learning systems: What do industry
  practitioners need?
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaMLHAI
282
779
0
13 Dec 2018
Learning Controllable Fair Representations
Learning Controllable Fair Representations
Jiaming Song
Pratyusha Kalluri
Aditya Grover
Shengjia Zhao
Stefano Ermon
FaML
98
180
0
11 Dec 2018
Individual Fairness in Hindsight
Individual Fairness in Hindsight
Swati Gupta
Vijay Kamble
FaML
89
63
0
10 Dec 2018
From Fair Decision Making to Social Equality
From Fair Decision Making to Social Equality
Hussein Mozannar
Mesrob I. Ohannessian
Nathan Srebro
90
99
0
07 Dec 2018
Probabilistic Verification of Fairness Properties via Concentration
Probabilistic Verification of Fairness Properties via Concentration
Osbert Bastani
Xin Zhang
Armando Solar-Lezama
FaMLFedML
81
72
0
02 Dec 2018
Racial categories in machine learning
Racial categories in machine learning
Sebastian Benthall
Bruce D. Haynes
FaML
88
125
0
28 Nov 2018
50 Years of Test (Un)fairness: Lessons for Machine Learning
50 Years of Test (Un)fairness: Lessons for Machine Learning
Ben Hutchinson
Margaret Mitchell
AILawFaML
95
364
0
25 Nov 2018
On Human Predictions with Explanations and Predictions of Machine
  Learning Models: A Case Study on Deception Detection
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Vivian Lai
Chenhao Tan
98
380
0
19 Nov 2018
Machine Decisions and Human Consequences
Machine Decisions and Human Consequences
Teresa Scantamburlo
A. Charlesworth
N. Cristianini
FaML
50
21
0
16 Nov 2018
Aequitas: A Bias and Fairness Audit Toolkit
Aequitas: A Bias and Fairness Audit Toolkit
Pedro Saleiro
Benedict Kuester
Loren Hinkson
J. London
Abby Stevens
Ari Anisfeld
Kit T. Rodolfa
Rayid Ghani
148
329
0
14 Nov 2018
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