Collaborative Filtering to Capture AI User’s Preferences as Norms

Marc Serramia Amoros*, Natalia Criado, Michael Luck

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)
53 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationPRIMA 2022
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 24th International Conference, Proceedings
EditorsReyhan Aydoğan, Natalia Criado, Victor Sanchez-Anguix, Jérôme Lang, Marc Serramia
Place of PublicationCham, Germany
PublisherSpringer
Pages669-678
Number of pages10
ISBN (Electronic)978-3-031-21203-1
ISBN (Print)9783031212024
DOIs
Publication statusPublished - 11 Nov 2022
EventPRIMA 2022: Principles and Practice of Multi-Agent Systems - Valencia, Spain
Duration: 16 Nov 202218 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13753 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePRIMA 2022: Principles and Practice of Multi-Agent Systems
Abbreviated titlePRIMA 2022
Country/TerritorySpain
CityValencia
Period16/11/202218/11/2022

Keywords

  • Norms
  • Collaborative filtering
  • Preferences
  • Privacy

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