CONCEPTS OF CULTURE-FAIRNESS
By R. L. Thorndike. 1971
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Ben Hutchinson
- Shira Mitchell
- Solon Barocas
- Simon Caton
- Margaret Mitchell
- Eric Potash
- F. Oswald
- Christian Haas
Citations in the corpus
- Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “For example, since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance (at school or work) [16, 120, 24, 33, 104, 57, 81, 58].”
- 50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, background): “in framing from unfairness to fairness . base rates (i.e., A is not independent of Y ) [24, 62]. test fairness . the contextual use of a test: requiring that the ratio ( TP + FP )/( TP + FN ) be equal for each for test cutoff x ∗ fair if threshold x ∗ 1 is used for group π 1 and threshold x ∗ 2 for…”
- ETS CONTRIBUTIONS TO THE QUANTITATIVE ASSESSMENT OF ITEM, TEST, AND SCORE FAIRNESS (2013, background): “…and Novick (1976) compared several models for assessing fair selection, including the regression model (Cleary, 1968), the constant ratio model (Thorndike, 1971), the conditional probability model (Cole, 1973), and the constant probability model (Linn, 1973) in the lead article in a special…”
- The New Faces of Fairness (2001, background): “Thorndike's model (Thorndike, 1971) indicates that a selection is fair if the ratio of those accepted to those who could succeed is the same across groups.”
- Determinants, Detection and Amelioration of Adverse Impact in Personnel Selection Procedures: Issues, Evidence and Lessons Learned (2001, background): “be successful absent a selection test (Thorndike 1971).”
- Fairness in Machine Learning: A Survey (2020, background): “…essential concepts such as protected/demographic variables (e.g., References [72, 73]), notions of group vs. individual fairness (e.g., References [257, 278]), impossibility conditions between fairness conditions [81], and metric-based fairness quantification (e.g., true positive rates [74]).”