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.

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

New framework for fair ranking with noisy protected attributes.

Algorithm ensures fair ranking by minority groups alongside majority groups.

New ranking system balances fairness and user utility.

Proposes causal modeling for intersectional fairness in rankings.

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

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…

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

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

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

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

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…

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 …

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…

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…

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…

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…

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

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…

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

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.

Simplifies fair PCA with fast, efficient solution.

This work improves fair tensor decomposition using a kernel criterion.

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…

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.

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…

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

New method uses causal thinking to make AI fairer decisions.

BIND removes background noise from binary matrices, improving detection accuracy and fairness.

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

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

Ranking recommendation algorithms across datasets using Bradley-Terry model

New conditions ensure Dantzig-Wolfe relaxation matches rank-constrained optimization problems.

Proposes a method for explaining ranking decisions in learning systems.

Improves Active Learning fairness and comparability across domains.

New approach makes survival analysis fairer without specifying sensitive features.

Estimating the dependences between random variables, and ranking them accordingly, is a prevalent problem in machine learning. Pursuing frequentist and information-theoretic approaches, we first show that the p-value and the mutual information can fail even in simplistic situations. We then propose two conditions for r…

A new measure PC allows fair comparison of AIWP and NWP models.

Two simple methods learn fair metrics from data to improve fairness in ML tasks.

Method ranks generative models without needing latent factor supervision.

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

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

Fair Mixup improves fairness in classifiers by interpolating between groups.