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Found 14 papers, 9 papers with code
Fair Classification with Partial Feedback: An Exploration-Based Data-Collection Approach
For any exploration strategy, the approach comes with guarantees that (1) all sub-populations are explored, (2) the fraction of false positives is bounded, and (3) the trained classifier converges to a "desired" classifier.
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM.
Bias in Evaluation Processes: An Optimization-Based Model
We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution.
Sampling Individually-Fair Rankings that are Always Group Fair
Fair ranking tasks, which ask to rank a set of items to maximize utility subject to satisfying group-fairness constraints, have gained significant interest in the Algorithmic Fairness, Information Retrieval, and Machine Learning literature.
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset.
Maximizing Submodular Functions for Recommendation in the Presence of Biases
In empirical evaluation, with both synthetic and real-world data, we observe that this algorithm improves the utility of the output subset for this family of submodular functions over baselines.
Fair Ranking with Noisy Protected Attributes
The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information retrieval, and machine learning literature.
Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints
Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection.
Fairness for AUC via Feature Augmentation
We evaluate fairAUC on synthetic and real-world datasets and find that it significantly improves AUC for the disadvantaged group relative to benchmarks maximizing overall AUC and minimizing bias between groups.
Fair Classification with Adversarial Perturbations
Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness.
Mitigating Bias in Set Selection with Noisy Protected Attributes
Subset selection algorithms are ubiquitous in AI-driven applications, including, online recruiting portals and image search engines, so it is imperative that these tools are not discriminatory on the basis of protected attributes such as gender or race.
The Effect of the Rooney Rule on Implicit Bias in the Long Term
Our main result is that, when the panel is constrained by the Rooney Rule, their implicit bias roughly reduces at a rate that is the inverse of the size of the shortlist--independent of the number of candidates, whereas without the Rooney Rule, the rate is inversely proportional to the number of candidates.
Interventions for Ranking in the Presence of Implicit Bias
Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group (e. g., defined by gender or race).
Toward Controlling Discrimination in Online Ad Auctions
To prevent this, we propose a constrained ad auction framework that maximizes the platform's revenue conditioned on ensuring that the audience seeing an advertiser's ad is distributed appropriately across sensitive types such as gender or race.
