Collaborative filtering to capture AI user's preferences as norms

TL;DR

This paper proposes using collaborative filtering, inspired by recommender systems, to automatically infer user preferences for AI norms, reducing manual effort and better capturing true user preferences.

Contribution

It introduces a novel approach applying collaborative filtering to norm preferences, addressing the challenge of minimal user involvement in customizing AI behaviors.

Findings

Potential to accurately infer user norms from system-wide preferences

Reduces user effort in setting AI preferences

Offers a scalable solution for personalized AI customization

Abstract

Customising AI technologies to each user's preferences is fundamental to them functioning well. Unfortunately, current methods require too much user involvement and fail to capture their true preferences. In fact, to avoid the nuisance of manually setting preferences, users usually accept the default settings even if these do not conform to their true preferences. Norms can be useful to regulate behaviour and ensure it adheres to user preferences but, while the literature has thoroughly studied norms, most proposals take a formal perspective. Indeed, while there has been some research on constructing norms to capture a user's privacy preferences, these methods rely on domain knowledge which, in the case of AI technologies, is difficult to obtain and maintain. We argue that a new perspective is required when constructing norms, which is to exploit the large amount of preference information readily available from whole systems of users. Inspired by recommender systems, we believe that collaborative filtering can offer a suitable approach to identifying a user's norm preferences without excessive user involvement.