Collaborative Filtering to Capture AI User’s Preferences as Norms

TY - CHAP

T1 - Collaborative Filtering to Capture AI User’s Preferences as Norms

AU - Serramia Amoros, Marc

AU - Criado, Natalia

AU - Luck, Michael

N1 - Funding Information: Research funded by the project SAIS: Secure AI AssistantS via Grant EP/T026723/1 from the UK Engineering and Physical Sciences Research Council. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022/11/11

Y1 - 2022/11/11

N2 - 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.

AB - 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.

KW - Norms

KW - Collaborative filtering

KW - Preferences

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=85142676882&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-21203-1_45

DO - 10.1007/978-3-031-21203-1_45

M3 - Conference paper

SN - 9783031212024

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 669

EP - 678

BT - PRIMA 2022

A2 - Aydoğan, Reyhan

A2 - Criado, Natalia

A2 - Sanchez-Anguix, Victor

A2 - Lang, Jérôme

A2 - Serramia, Marc

PB - Springer

CY - Cham, Germany

T2 - PRIMA 2022: Principles and Practice of Multi-Agent Systems

Y2 - 16 November 2022 through 18 November 2022

ER -