  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.
