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：将人工智能技术定制化地适配每位用户偏好是其良好运行的基础。遗憾的是，当前方法要求用户过多参与，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，即使这些设置并不符合其真实偏好。规范可有效调节行为并确保其遵循用户偏好，但现有文献虽已深入研究规范，多数提案仍停留在形式化视角。尽管已有研究尝试通过构建规范来捕捉用户隐私偏好，但这些方法依赖领域知识——在人工智能技术场景中，这种知识既难获取也难以维护。我们认为，构建规范需要新视角，即利用整个用户系统中现成可获取的海量偏好信息。受推荐系统启发，我们相信协同过滤无需用户过多参与，即可提供识别用户规范偏好的合适途径。