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There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias.
Scientific selection of employees
PW Gerhardt · 1916
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Testing the fitness of your employees
William F Kemble · 1916
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The measurement of intelligence: An explanation of and a complete guide for the use of the Stanford revision and extension of the Binet-Simon intelligence scale
Lewis Madison Terman · 1916
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The psychology of human differences
Leona E Tyler · 1947
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Fair employment and housing act, 1959
California State Legislature · 1959
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The servants of power: A history of the use of social science in American industry
Loren Baritz · 1960
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The Myers-Briggs type indicator
Isabel Briggs Myers · 1962
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Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings
Warren T Norman · 1963
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Civil rights act, 1964
U.S. Congress · 1964
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Racial differences on selection instruments related to subsequent job performance
Edward Ruda and Lewis E Albright · 1968
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A history of psychological testing
Philip Hunter DuBois · 1970
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Uniform guidelines on employee selection procedures
Equal Employment Opportunity Commission, Civil Service Commission, et al · 1978
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The triumph of evolution: The heredity–environment controversy, 1900–1941
Hamilton Cravens · 1978
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Differential pass-fail rates in employment testing: Statistical proof under Title VII
Elaine W Shoben · 1978
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On the use of statistics in employment discrimination cases
Richard M Cohn · 1979
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Statistical laws and the use of statistics in law: A rejoinder to Professor Shoben
Richard M Cohn · 1979
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Personnel selection and classification systems
Marvin D Dunnette and Walter C Borman · 1979
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In defense of disparate impact analysis under Title VII: A reply to Dr. Cohn
Elaine W Shoben · 1979
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Employment tests and employment discrimination: A dissenting psychological opinion
Craig Haney · 1982
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Fairness in employment testing: Validity generalization, minority issues, and the General Aptitude Test Battery
National Research Council et al · 1989
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Job evaluation and gender: The case of university faculty
Jim Sidanius and Marie Crane · 1989
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Americans with disabilities act, 1990
U.S. Congress · 1990
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Civil rights act, 1991
U.S. Congress · 1991
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Personnel selection and assessment: Individual and organizational perspectives
Heinz Schuler, James L Farr, and Mike Smith · 1993
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Sex discrimination in restaurant hiring: An audit study
David Neumark, Roy J Bank, and Kyle D Van Nort · 1996
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Employment discrimination against older workers: An experimental study of hiring practices
Marc Bendick Jr, Charles W Jackson, and J Horacio Romero · 1997
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Psychology and industrial efficiency
Hugo Munsterberg · 1998
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The elements of statistical learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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The adverse impact of high stakes testing on minority students: Evidence from 100 years of test data
George F Madaus and Marguerite Clarke · 2001
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Differential validity, differential prediction, and college admission testing: A comprehensive review and analysis. Research report no. 2001-6
John W Young · 2001
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Field experiments of discrimination in the market place
Peter A Riach and Judith Rich · 2002
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Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination
Marianne Bertrand and Sendhil Mullainathan · 2004
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Meta-analysis of coefficient alpha
Michael C Rodriguez and Yukiko Maeda · 2006
Cited alongside, same era.
Are the uniform guidelines outdated? federal guidelines, professional standards, and validity generalization (vg)
Daniel A Biddle · 2008
Cited alongside, same era.
Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
Cited alongside, same era.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Cited alongside, same era.
Internal facial features are signals of personality and health
Robin SS Kramer and Robert Ward · 2010
Cited alongside, same era.
The uniform guidelines are a detriment to the field of personnel selection
Michael A Mcdaniel, Sven Kepes, and George C Banks · 2011
Cited alongside, same era.
Antidiscriminatory algorithms
Stephanie Bornstein · 2018
Later among the works it cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Later among the works it cites.
Artificial intelligence in human resources management: Challenges and a path forward
Peter Cappelli, Prasanna Tambe, and Valery Yakubovich · 2018
Later among the works it cites.
Ranking with fairness constraints
L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi · 2018
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Bias and productivity in humans and algorithms: Theory and evidence from resume screening
Bo Cowgill · 2018
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Principles for the validation and use of personnel selection procedures
Society for Industrial, Organizational Psychology (US), and American Psychological Association. Division of Industrial-Organizational Psychology · 2018
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Reply to request for public comment on plan for retrospective analysis of significant regulations pursuant to executive order 13563, 2011
Eduardo Salas · 2011
Cited alongside, same era.
Developing the research basis for controlling bias in hiring
Marc Bendick and Ana P Nunes · 2012
Cited alongside, same era.
New directions in assessing performance potential of individuals and groups: Workshop summary
National Research Council et al · 2013
Cited alongside, same era.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Big data recommendations for industrial–organizational psychology
Richard A Guzzo, Alexis A Fink, Eden King, Scott Tonidandel, and Ronald S Landis · 2015
Cited alongside, same era.
Later among the works it cites.
Expanding employment success for people with disabilities
Jim Fruchterman and Joan Melllea · 2018
Later among the works it cites.
Letter to U.S. Equal Employment Opportunity Commission, 2018
Kamala D. Harris, Patty Murray, and Elizabeth Warren · 2018
Later among the works it cites.
The science behind the Koru model of predictive hiring for fit
Josh Jarrett and Sarah Croft · 2018
Later among the works it cites.
Big data and artificial intelligence: New challenges for workplace equality
Pauline T Kim · 2018
Later among the works it cites.
Does mitigating ml’s impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Later among the works it cites.
Mitigating bias in AI models
Ruchir Puri · 2018
Later among the works it cites.
Bots, bias and big data: Artificial intelligence, algorithmic bias and disparate impact liability in hiring practices
McKenzie Raub · 2018
Later among the works it cites.
Racial influence on automated perceptions of emotions
Lauren Rhue · 2018
Later among the works it cites.
Microsoft improves facial recognition technology to perform well across all skin tones, genders
John Roach · 2018
Later among the works it cites.
Artificial intelligence video interview act, 2019
Illinois General Assembly · 2019
Closest in time.
Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Lisa Feldman Barrett, Ralph Adolphs, Stacy Marsella, Aleix M Martinez, and Seth D Pollak · 2019
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Should companies use AI to assess job candidates?
Tomas Chamorro-Prezumic and Reece Akhtar · 2019
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Diversity & inclusion technology: The rise of a transformative market
Stacia Sherman Garr and Carole Jackson · 2019
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi · 2019
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Toward fairness in ai for people with disabilities: A research roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hannah Wallach, and Meredith Ringel Morris · 2019
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Measuring algorithmic fairness
Deborah Hellman · 2019
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Can AI solve the diversity problem in the tech industry? mitigating noise and bias in employment decision-making
Kimberly Houser · 2019
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50 years of test (un) fairness: Lessons for machine learning
Ben Hutchinson and Margaret Mitchell · 2019
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Discrimination in the age of algorithms
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Cass R Sunstein · 2019
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Problem formulation and fairness
Samir Passi and Solon Barocas · 2019
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
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The paradox of automation as anti-bias intervention
Ifeoma Ajunwa · 2020
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Is algorithmic affirmative action legal?
Jason R Bent · 2020
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Manipulating opportunity
Pauline T Kim · 2020
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What does it mean to solve the problem of discrimination in hiring? social, technical and legal perspectives from the uk on automated hiring systems
Javier Sanchez-Monedero, Lina Dencik, and Lilian Edwards · 2020
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