Convenience wrapper around pareto_frontier() for selection-system alternatives
evaluated on utility, fairness, and optionally validity.
Usage
utility_fairness_frontier(utility, fairness, validity = NULL)
Arguments
- utility
Numeric vector of utility values to maximize.
- fairness
Numeric vector of fairness values to maximize, for example an
adverse-impact ratio where larger values indicate smaller subgroup disparity.
- validity
Optional numeric vector of validity values to maximize.
Value
A data frame with frontier membership.
References
De Corte, W., Lievens, F., & Sackett, P. R. (2007). Combining predictors to
achieve optimal trade-offs between selection quality and adverse impact.
Journal of Applied Psychology, 92, 1380-1393.
Tippins, N. T., Oswald, F. L., & McPhail, S. M. (2021). Scientific, legal,
and ethical concerns about AI-based personnel selection tools: A call to
action. Personnel Assessment and Decisions, 7(2), Article 1.
Examples
# Literature: De Corte, Lievens, and Sackett (2007); Tippins, Oswald, and McPhail (2021).
utility_fairness_frontier(utility = c(100, 120, 90), fairness = c(.80, .70, .95))
#> utility fairness pareto_efficient
#> 1 100 0.80 TRUE
#> 2 120 0.70 TRUE
#> 3 90 0.95 TRUE
