Informed consent is a core cornerstone of ethics in human subject research.
Through the informed consent process, participants learn about the study
procedure, benefits, risks, and more to make an informed decision. However,
recent studies showed that current practices might lead to uninformed decisions
and expose participants to unknown risks, especially in online studies. Without
the researcher's presence and guidance, online participants must read a lengthy
form on their own with no answers to their questions. In this paper, we
examined the role of an AI-powered chatbot in improving informed consent
online. By comparing the chatbot with form-based interaction, we found the
chatbot improved consent form reading, promoted participants' feelings of
agency, and closed the power gap between the participant and the researcher.
Our exploratory analysis further revealed the altered power dynamic might
eventually benefit study response quality. We discussed design implications for
creating AI-powered chatbots to offer effective informed consent in broader
settings.

本文探讨了 AI 聊天机器人在改善在线知情同意书中的作用，通过与表格交互的比较，我们发现聊天机器人改善了同意书的阅读，促进参与者的代理感，并缩小了参与者与研究者之间的权力差距，并发现权力动态的改变最终可能有利于研究响应质量。

AI 动力聊天机器人：提高在线知情同意书阅读的效率

Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot

Informed consent has become increasingly salient for data privacy and its
regulation. Entities from governments to for-profit companies have addressed
concerns about data privacy with policies that enumerate the conditions for
personal data storage and transfer. However, increased enumeration of and
transparency in data privacy policies has not improved end-users' comprehension
of how their data might be used: not only are privacy policies written in legal
language that users may struggle to understand, but elements of these policies
may compose in such a way that the consequences of the policy are not
immediately apparent.
We present a framework that uses Answer Set Programming (ASP) -- a type of
logic programming -- to formalize privacy policies. Privacy policies thus
become constraints on a narrative planning space, allowing end-users to
forward-simulate possible consequences of the policy in terms of actors having
roles and taking actions in a domain. We demonstrate through the example of the
Health Insurance Portability and Accountability Act (HIPAA) how to use the
system in various ways, including asking questions about possibilities and
identifying which clauses of the law are broken by a given sequence of events.

使用 ASP（Answer Set Programming）的框架，将隐私政策形式化为叙事规划空间上的约束，并通过 HIPAA 的范例演示如何使用该系统来询问可能性并确定给定事件序列中违反法律条款的内容。

通过答案集编程生成叙述来探索隐私政策的后果

Exploring Consequences of Privacy Policies with Narrative Generation via Answer Set Programming

Recent work in natural language processing (NLP) has focused on ethical
challenges such as understanding and mitigating bias in data and algorithms;
identifying objectionable content like hate speech, stereotypes and offensive
language; and building frameworks for better system design and data handling
practices. However, there has been little discussion about the ethical
foundations that underlie these efforts. In this work, we study one ethical
theory, namely deontological ethics, from the perspective of NLP. In
particular, we focus on the generalization principle and the respect for
autonomy through informed consent. We provide four case studies to demonstrate
how these principles can be used with NLP systems. We also recommend directions
to avoid the ethical issues in these systems.

本文从自然语言处理的角度，研究了康德的义务论伦理学中的普遍化原则和知情同意的尊重，提供了四个案例研究，以说明这些原则如何与自然语言处理系统结合。同时提出了避免这些系统中的道德问题的建议。