Human-AI co-creativity involves humans and AI collaborating on a shared
creative product as partners. In many existing co-creative systems, users
communicate with the AI using buttons or sliders. However, typically, the AI in
co-creative systems cannot communicate back to humans, limiting their potential
to be perceived as partners. This paper starts with an overview of a
comparative study with 38 participants to explore the impact of AI-to-human
communication on user perception and engagement in co-creative systems and the
results show improved collaborative experience and user engagement with the
system incorporating AI-to-human communication. The results also demonstrate
that users perceive co-creative AI as more reliable, personal and intelligent
when it can communicate with the users. The results indicate a need to identify
potential ethical issues from an engaging communicating co-creative AI. Later
in the paper, we present some potential ethical issues in human-AI co-creation
and propose to use participatory design fiction as the research methodology to
investigate the ethical issues associated with a co-creative AI that
communicates with users.

本研究探讨人工智能和人类合作创意产品时，AI 对人的回馈对用户体验和参与度的影响，并通过人机合作沟通提出共创 AI 潜在的伦理问题。

AI 人类协同创造中的伦理问题识别

Identifying Ethical Issues in AI Partners in Human-AI Co-Creation

In order to interpret the communicative intents of an utterance, it needs to
be grounded in something that is outside of language; that is, grounded in
world modalities. In this paper, we argue that dialogue clarification
mechanisms make explicit the process of interpreting the communicative intents
of the speaker's utterances by grounding them in the various modalities in
which the dialogue is situated. This paper frames dialogue clarification
mechanisms as an understudied research problem and a key missing piece in the
giant jigsaw puzzle of natural language understanding. We discuss both the
theoretical background and practical challenges posed by this problem and
propose a recipe for obtaining grounding annotations. We conclude by
highlighting ethical issues that need to be addressed in future work.

本文提出对话澄清机制是自然语言理解中一个未被充分研究的关键问题，并讨论了该问题的理论背景、实际挑战和获取基础注释的方法。同时，还提出了需要在未来研究中解决的伦理问题。

基于场景的澄清标注配方

A recipe for annotating grounded clarifications

Datasets have played a foundational role in the advancement of machine
learning research. They form the basis for the models we design and deploy, as
well as our primary medium for benchmarking and evaluation. Furthermore, the
ways in which we collect, construct and share these datasets inform the kinds
of problems the field pursues and the methods explored in algorithm
development. However, recent work from a breadth of perspectives has revealed
the limitations of predominant practices in dataset collection and use. In this
paper, we survey the many concerns raised about the way we collect and use data
in machine learning and advocate that a more cautious and thorough
understanding of data is necessary to address several of the practical and
ethical issues of the field.

本文综述了在机器学习中收集和使用数据的许多问题和担忧，并提倡通过更谨慎和深入的数据理解来解决实践和伦理问题。