publication . Preprint . 2019

Entrofy Your Cohort: A Data Science Approach to Candidate Selection

Huppenkothen, D.; McFee, B.; Norén, L.;
Open Access English
  • Published: 08 May 2019
Abstract
Selecting a cohort from a set of candidates is a common task within and beyond academia. Admitting students, awarding grants, choosing speakers for a conference are situations where human biases may affect the make-up of the final cohort. We propose a new algorithm, Entrofy, designed to be part of a larger decision making strategy aimed at making cohort selection as just, quantitative, transparent, and accountable as possible. We suggest this algorithm be embedded in a two-step selection procedure. First, all application materials are stripped of markers of identity that could induce conscious or sub-conscious bias. During blind review, the committee selects all...
Subjects
free text keywords: Computer Science - Computers and Society, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Physics Education
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39 references, page 1 of 3

1. Bohnet I. What works: Gender equality by design. Harvard University Press; 2016.

2. Greenwald A, Banaji M. Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes. Psychological Review. 1995;102:4-27. [OpenAIRE]

3. Reskin B. The Proximate Causes of Employment Discrimination. Contemporary Sociology. 2000;29:319-28. [OpenAIRE]

4. Riach P, Rich J. Field Experiments of Discrimination in the Market Place. The Economic Journal. 2002;112(483):F480-F518. [OpenAIRE]

5. Gorman E. Gender Stereotypes, Same-Gender Preferences, and Organizational Variation in the Hiring of Women: Evidence from Law Firms. American Sociological Review. 2005;70:702-28.

6. Pager D, Quillian L. Walking the Talk? What Employers Say versus What They Do. American Sociological Review. 2005;70(3):355-380.

7. Krieger L, Fiske S. Behavioral Realism in Employment Discrimination Law: Implicit Bias and Disparate Treatment. California Law Review. 2006;94(4):997-1062.

8. Rooth DO. Automatic associations and discrimination in hiring: Real world evidence. Labour Economics. 2010;17(3):523-534.

9. Kuncel NR, Connelly BS, Klieger DM, Ones DS. Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis. Journal of applied psychology. 2013;98(6):1060-1072. [OpenAIRE]

10. Grove WM, Meehl PE. Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy. Psychology, Public Policy, and Law. 1996;2:293-323. [OpenAIRE]

11. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. Clinical versus mechanical prediction: a meta-analysis. Psychological assessment. 2000;12(1):19. [OpenAIRE]

12. Pulakos ED, Schmitt N, Whitney D, Smith M. Individual differences in interviewer ratings: The impact of standardization, consensus discussion, and sampling error on the validity of a structured interview. Personnel Psychology. 1996;49(1):85-102.

13. Kausel EE, Culbertson SS, Madrid HP. Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions. Organizational Behavior and Human Decision Processes. 2016;137:27-44. [OpenAIRE]

14. Highhouse S. Understanding and improving job-finalist choice: The relevance of behavioral decision research. Human Resource Management Review. 1998;7(4):449-470.

15. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. Science faculty's subtle gender biases favor male students. Proceedings of the National Academy of Sciences. 2012;109(41):16474-16479. doi:10.1073/pnas.1211286109. [OpenAIRE]

39 references, page 1 of 3
Abstract
Selecting a cohort from a set of candidates is a common task within and beyond academia. Admitting students, awarding grants, choosing speakers for a conference are situations where human biases may affect the make-up of the final cohort. We propose a new algorithm, Entrofy, designed to be part of a larger decision making strategy aimed at making cohort selection as just, quantitative, transparent, and accountable as possible. We suggest this algorithm be embedded in a two-step selection procedure. First, all application materials are stripped of markers of identity that could induce conscious or sub-conscious bias. During blind review, the committee selects all...
Subjects
free text keywords: Computer Science - Computers and Society, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Physics Education
Related Organizations
Download from
39 references, page 1 of 3

1. Bohnet I. What works: Gender equality by design. Harvard University Press; 2016.

2. Greenwald A, Banaji M. Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes. Psychological Review. 1995;102:4-27. [OpenAIRE]

3. Reskin B. The Proximate Causes of Employment Discrimination. Contemporary Sociology. 2000;29:319-28. [OpenAIRE]

4. Riach P, Rich J. Field Experiments of Discrimination in the Market Place. The Economic Journal. 2002;112(483):F480-F518. [OpenAIRE]

5. Gorman E. Gender Stereotypes, Same-Gender Preferences, and Organizational Variation in the Hiring of Women: Evidence from Law Firms. American Sociological Review. 2005;70:702-28.

6. Pager D, Quillian L. Walking the Talk? What Employers Say versus What They Do. American Sociological Review. 2005;70(3):355-380.

7. Krieger L, Fiske S. Behavioral Realism in Employment Discrimination Law: Implicit Bias and Disparate Treatment. California Law Review. 2006;94(4):997-1062.

8. Rooth DO. Automatic associations and discrimination in hiring: Real world evidence. Labour Economics. 2010;17(3):523-534.

9. Kuncel NR, Connelly BS, Klieger DM, Ones DS. Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis. Journal of applied psychology. 2013;98(6):1060-1072. [OpenAIRE]

10. Grove WM, Meehl PE. Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy. Psychology, Public Policy, and Law. 1996;2:293-323. [OpenAIRE]

11. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. Clinical versus mechanical prediction: a meta-analysis. Psychological assessment. 2000;12(1):19. [OpenAIRE]

12. Pulakos ED, Schmitt N, Whitney D, Smith M. Individual differences in interviewer ratings: The impact of standardization, consensus discussion, and sampling error on the validity of a structured interview. Personnel Psychology. 1996;49(1):85-102.

13. Kausel EE, Culbertson SS, Madrid HP. Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions. Organizational Behavior and Human Decision Processes. 2016;137:27-44. [OpenAIRE]

14. Highhouse S. Understanding and improving job-finalist choice: The relevance of behavioral decision research. Human Resource Management Review. 1998;7(4):449-470.

15. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. Science faculty's subtle gender biases favor male students. Proceedings of the National Academy of Sciences. 2012;109(41):16474-16479. doi:10.1073/pnas.1211286109. [OpenAIRE]

39 references, page 1 of 3
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