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Found 8 papers, 4 papers with code
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
We clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system.
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation
In this paper, we present a step-by-step procedure integrating three elements: (a) a framework for the moral assessment of what fairness means in a given context, based on the recently proposed general principle of "Fair equality of chances" (FEC) (b) a mapping of the assessment's results to established statistical group fairness criteria, and (c) a method for integrating the thus-defined fairness into optimal decision making.
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems.
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs
This allows us to derive a fairness score that we then compare to the decision maker's utility.
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency
We show that group-specific threshold rules are optimal for PPV parity and FOR parity, similar to well-known results for other group fairness criteria.
Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory
For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness.
A Systematic Approach to Group Fairness in Automated Decision Making
While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice.
On the Moral Justification of Statistical Parity
This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
