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.

翻译：将AI技术定制到每个用户的偏好是其良好运行的基础。遗憾的是，当前方法需要过多用户参与，且无法捕捉其真实偏好。事实上，为避免手动设置偏好的麻烦，用户通常接受默认设置，即使这些设置不符合其真实偏好。规范可用于调节行为并确保其符合用户偏好，但尽管文献已对规范进行了深入研究，大多数提案仍采用形式化视角。确实，虽然已有一些关于构建规范以捕获用户隐私偏好的研究，但这些方法依赖于领域知识，而在AI技术背景下，这些知识难以获取和维护。我们认为，在构建规范时需要一种新视角：利用整个用户系统中可获取的大量偏好信息。受推荐系统启发，我们相信协同过滤能在无需过多用户参与的情况下，提供识别用户规范偏好的合适方法。