Fair Personalization

By L. Elisa Celis, Nisheeth K. Vishnoi. 2017

18 citations recorded by the source corpus.

Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that such personalization can propagate societal or systemic biases, which has led to calls for regulatory mechanisms and algorithms to combat inequality. Here we propose a rigorous algorithmic framework that allows for the possibility to control biased or discriminatory personalization with respect to sensitive attributes of users without losing all of the benefits of personalization.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Multiwinner Voting with Fairness Constraints (2017, background): “, 2018b; Yang and Stoyanovich, 2017] and personalization [Celis and Vishnoi, 2017].”
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees (2018, methodology): “To handle this problem, we consider linear fairness constraints , which have been considered in other fundamental algorithmic problems including sampling [11, 10, 13], ranking [14], voting [12], and personalization [15].”
The Unfair Externalities of Exploration (2017, methodology): “We initialize a formal study of this issue, continuing an active line of work on unfairness and bias in machine learning [4, 5, 7, 8, 11].”