Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
This tackles the ethical issue of user autonomy in recommender systems, which is incremental as it builds on existing concerns about manipulation.
The paper addresses the problem of preference manipulation in recommender systems, arguing that solutions must incorporate meta-preferences to respect user autonomy and avoid manipulation.
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.