Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
Information Retrieval
2022-09-27 v1 Artificial Intelligence
Computers and Society
Machine Learning
Abstract
Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them. Further a recommender can better predict what a user will do by making its users more predictable. Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences (preferences over another preference) in order to respect the autonomy of the user and not be manipulative.
Cite
@article{arxiv.2209.11801,
title = {Solutions to preference manipulation in recommender systems require knowledge of meta-preferences},
author = {Hal Ashton and Matija Franklin},
journal= {arXiv preprint arXiv:2209.11801},
year = {2022}
}
Comments
Accepted at the RecSys-22 5th FAccTRec Workshop on Responsible Recommendation held at the 16th ACM Conference on Recommender Systems, 3 pages, 1 figure