Search Results for author:
Found 8 papers, 5 papers with code
Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance
Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness.
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
The success of the collective is measured by the increase in test-time recommendations of the targeted song.
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.
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
By qualitative analysis, and through a simulation example of recommender systems, we show which specific types of ML biases are affected by each type of feedback loop.
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.
