Algorithmic Fairness

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29 papers in the last four weeks, up 71% on the four weeks before. 0.3% of all new papers.

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Unfair Utilities and First Steps Towards Improving Them
Jun 1, 2023Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas PetersValue of Information Algorithmic Fairness
Dept. of Mathematical Sciences University of Copenhagen Copenhagen, Denmark · Dept. of Mathematics ETH Zurich Zurich, Switzerland
Proportionally Representative Clustering
Apr 27, 2023Haris Aziz, Barton E. Lee, Sean Morota Chu +1Clustering Algorithmic Fairness
UNSW Sydney · ETH Zürich · Northwestern University
On the Societal Impact of Machine Learning
Date pendingJoachim BaumannAlgorithmic Fairness Algorithmic Bias
Faculty of Business, Economics and Informatics of the University of Zurich
Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions
Date pendingDuraimurugan RajamanickamFinancial Services Causal Effect Estimation
VP, Artificial Intelligence, Hudson Valley Credit Union, Poughkeepsie, NY · PhD Candidate, Causal Machine Learning, University of Arkansas at Little Rock
Assessing Predictive Models for Fairness Based on Activity-Space Patterns
Date pendingFrancesco Lettich, Mario A. Nascimento, Chiara Pugliese +1Algorithmic Fairness Group Fairness
Institute of Information Science and Technologies, National Research Council, Pisa, Italy · Department of Computing Science, University of Alberta, Edmonton, Canada · Institute of Informatics and Telematics, National Research Council, Pisa, Italy

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Unfair Utilities and First Steps Towards Improving Them

Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters

Dept. of Mathematical Sciences University of Copenhagen Copenhagen, Denmark · Dept. of Mathematics ETH Zurich Zurich, Switzerland

Value of Information Algorithmic Fairness

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Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, we in- stead suggest that fairness can be directly analyzed as a property of the utility function. Instead of imposing fairness constraints on the policy, we suggest to simply maximize a utility function satisfying certain fairness properties. Concretely, we define value of information fairness, which prescribes that there must not be an incentive to infer the protected attribute. This principle sug- gests modifying utility functions such that they satisfy value of information fairness. We describe how such modifications can be achieved and discuss consequences for the corresponding optimal policies. We apply our framework to thought experiments and the COMPAS data, demonstrating that focusing on utility functions sometimes provides answers that better align with intuitive judg- ments about what is fair. Moreover, we are not aware of any intuitively fair policy that violates value of information fairness; and when we find that value of information fairness recommends an intuitively unfair policy, no realizable policy is intuitively fair.

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Latest papers 275

Unfair Utilities and First Steps Towards Improving Them

Proportionally Representative Clustering

On the Societal Impact of Machine Learning

Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions

Assessing Predictive Models for Fairness Based on Activity-Space Patterns