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Entrofy your cohort: A transparent method for diverse cohort selection.
Daniela Huppenkothen1,2, Brian McFee3,4, Laura Norén5
1Department of Astronomy, DIRAC Institute, University of Washington, Seattle, WA, United States of America.
Plos One
|July 28, 2020
Summary
Introducing entrofy, a novel algorithm for fair cohort selection. This tool aids human decision-making in academic and professional settings, promoting transparency and accountability in candidate selection processes.
Area of Science:
- Decision Science
- Computer Science
- Social Science
Background:
- Human biases can influence cohort selection in academia and grant awarding.
- Current selection processes may lack transparency and accountability.
Purpose of the Study:
- To introduce entrofy, a new algorithm for just and transparent cohort selection.
- To embed entrofy within a two-step decision-making strategy to mitigate bias.
Main Methods:
- A two-step selection procedure: merit review followed by entrofy algorithm application.
- Entrofy optimizes cohort composition based on user-defined diversity criteria.
- The algorithm solves tie-breaking problems with provable performance guarantees.
Main Results:
- Entrofy successfully selects cohorts approximating pre-defined target proportions.
- Demonstrated effectiveness in simulated application sets and a case study (Astro Hack Week).
- The two-stage process separates merit assessment from diversity considerations.
Conclusions:
- Entrofy enhances transparency and auditability in cohort selection.
- Separating merit and diversity assessment aids in explaining selection outcomes.
- The algorithm provides a tool for accountable decision-making, though it doesn't eliminate all bias.
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