arXiv research

Paper tackles underranking in group-fair ranking systems, proving a trade-off and presenting an algorithm.

Algorithm samples fair rankings to ensure individual fairness while maintaining group fairness.

Algorithm ensures fair ranking by minority groups alongside majority groups.

New framework for fair ranking with noisy protected attributes.

Paper tackles fair low-rank approximation and column subset selection.

A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.

Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job plac…

While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the l…

Proposes a method to balance fairness and utility in ranking models.

This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.

New algorithm for fair ranking in contextual bandits with concave rewards.

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. I…

This work transfers fairness notions from binary classification to learning to rank.

New ranking system balances fairness and user utility.

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them? In this paper we offer …

Proposes causal modeling for intersectional fairness in rankings.

Develops a new fairness learning approach for multi-task regression models.

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discriminat…

Many applications of AI involve scoring individuals using a learned function of their attributes. These predictive risk scores are then used to take decisions based on whether the score exceeds a certain threshold, which may vary depending on the context. The level of delegation granted to such systems in critical appl…

This work improves fair tensor decomposition using a kernel criterion.

A new method for fair PCA ensures balanced error across groups.

New methods ensure fair rankings in web-scale recommender systems.

New algorithm reduces unfairness in bandit problems by balancing exploration and exploitation.

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the res…

One of the most critical problems in weight-sharing neural architecture search is the evaluation of candidate models within a predefined search space. In practice, a one-shot supernet is trained to serve as an evaluator. A faithful ranking certainly leads to more accurate searching results. However, current methods are…

How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combin…

Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires t…

Study examines impact of fairness penalties on clinical risk prediction models.

Ranking recommendation algorithms across datasets using Bradley-Terry model

Study fairness in ordinal regression using threshold models.

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

Improves Active Learning fairness and comparability across domains.

Study shows how online personalization can lead to unfair models due to biased user responses.

Simplifies fair PCA with fast, efficient solution.

Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…

New framework tackles fairness in link prediction beyond demographic parity.

New concept of within-group fairness improves AI fairness without sacrificing accuracy.

New welfare-based fairness notions align with existing error rate balance and predictive parity.

A new fairness metric for decision-making algorithms, conditioning on known fair variables.

The study redefines algorithmic fairness as a sociotechnical concept.

Paper proposes a unified sparsity-based framework for evaluating algorithmic fairness.

Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.

FCA improves fair clustering by optimizing utility and fairness.

We introduce the study of fairness in multi-armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admission or mortgages), a worse applicant is never favored over a better one, despite a learning algorithm's uncertainty over the true payoffs. We…

New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.

FaiREE provides fair classification with guarantees for small datasets.

New method uses causal thinking to make AI fairer decisions.

New fair PCA method using streaming algorithms with statistical guarantees.