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.

个性化 AI 技术的定制对其良好运行至关重要，当前的方法需要太多用户参与，无法真正捕捉其偏好。我们认为需要一种新的视角来构建规范，利用整个用户系统中大量可用的偏好信息，启发于推荐系统，我们相信协同过滤可以提供一种适合的方法来识别用户的规范偏好，避免过多用户参与。

协同过滤捕捉 AI 用户的偏好作为规范

Collaborative filtering to capture AI user's preferences as norms

ChatGPT, an AI chatbot, has gained popularity for its capability in
generating human-like responses. However, this feature carries several risks,
most notably due to its deceptive behaviour such as offering users misleading
or fabricated information that could further cause ethical issues. To better
understand the impact of ChatGPT on our social, cultural, economic, and
political interactions, it is crucial to investigate how ChatGPT operates in
the real world where various societal pressures influence its development and
deployment. This paper emphasizes the need to study ChatGPT "in the wild", as
part of the ecosystem it is embedded in, with a strong focus on user
involvement. We examine the ethical challenges stemming from ChatGPT's
deceptive human-like interactions and propose a roadmap for developing more
transparent and trustworthy chatbots. Central to our approach is the importance
of proactive risk assessment and user participation in shaping the future of
chatbot technology.

探讨 ChatGPT 对社交，文化，经济和政治互动的影响，着重于用户参与的重要性，提出了更加透明可信的聊天机器人的发展路线图，包括积极风险评估和用户参与。

欺骗性人工智能生态系统：以 ChatGPT 为例

Deceptive AI Ecosystems: The Case of ChatGPT

User trust in Artificial Intelligence (AI) enabled systems has been
increasingly recognized and proven as a key element to fostering adoption. It
has been suggested that AI-enabled systems must go beyond technical-centric
approaches and towards embracing a more human centric approach, a core
principle of the human-computer interaction (HCI) field. This review aims to
provide an overview of the user trust definitions, influencing factors, and
measurement methods from 23 empirical studies to gather insight for future
technical and design strategies, research, and initiatives to calibrate the
user AI relationship. The findings confirm that there is more than one way to
define trust. Selecting the most appropriate trust definition to depict user
trust in a specific context should be the focus instead of comparing
definitions. User trust in AI-enabled systems is found to be influenced by
three main themes, namely socio-ethical considerations, technical and design
features, and user characteristics. User characteristics dominate the findings,
reinforcing the importance of user involvement from development through to
monitoring of AI enabled systems. In conclusion, user trust needs to be
addressed directly in every context where AI-enabled systems are being used or
discussed. In addition, calibrating the user-AI relationship requires finding
the optimal balance that works for not only the user but also the system.

对用户信任、影响因素和测量方法进行的 23 个实证研究的综述确认了定义信任的多种方法和用户特征对 AI 启用系统中的信任产生的影响等主题，从而为未来技术和设计策略、研究和举措提供了洞察。