Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Autor:	Halabi, Marwa El, Mitrović, Slobodan, Norouzi-Fard, Ashkan, Tardos, Jakab, Tarnawski, Jakub
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Data Structures and Algorithms
Druh dokumentu:	Working Paper
Popis:	Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made the design of fair machine learning algorithms increasingly important. In this work we address the question: Is it possible to create fair summaries for massive datasets? To this end, we develop the first streaming approximation algorithms for submodular maximization under fairness constraints, for both monotone and non-monotone functions. We validate our findings empirically on exemplar-based clustering, movie recommendation, DPP-based summarization, and maximum coverage in social networks, showing that fairness constraints do not significantly impact utility. Comment: Accepted to NeurIPS 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.07431 Zobrazit plný text záznamu View this record from Arxiv