Deep Learning for Dialogue Systems: Chit-Chat and Beyond

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, there are important differences: the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response all complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz (2022), “Fairness in Information Access Systems”, Foundations and Trends® in Information Retrieval: Vol. 16, No. 1-2, pp 1–177. DOI: 10.1561/1500000079. ©2022 M. D. Ekstrand et al. Full text available at: http://dx.doi.org/10.1561/1500000079