The ethics of AI as both material and medium for interaction remains in murky
waters within the context of musical and artistic practice. The
interdisciplinarity of the field is revealing matters of concern and care,
which necessitate interdisciplinary methodologies for evaluation to trouble and
critique the inheritance of "residue-laden" AI-tools in musical applications.
Seeking to unsettle these murky waters, this paper critically examines the
example of Holly+, a deep neural network that generates raw audio in the
likeness of its creator Holly Herndon. Drawing from theoretical concerns and
considerations from speculative feminism and care ethics, we care-fully trouble
the structures, frameworks and assumptions that oscillate within and around
Holly+. We contribute with several considerations and contemplate future
directions for integrating speculative feminism and care into musical-AI agent
and system design, derived from our critical feminist examination.

AI 在音乐和艺术实践中的伦理问题仍然模糊不清，而该论文通过对 Holly + 的批判性研究，运用推测性女性主义和关怀伦理等理论考虑，对音乐 AI 代理和系统设计提出了关于伦理问题和集成推测性女性主义和关怀伦理的思考。

关怀之难与音乐人工智能：对女性主义音乐人工智能的思考

Caring Trouble and Musical AI: Considerations towards a Feminist Musical  AI

To facilitate the widespread acceptance of AI systems guiding decision-making
in real-world applications, it is key that solutions comprise trustworthy,
integrated human-AI systems. Not only in safety-critical applications such as
autonomous driving or medicine, but also in dynamic open world systems in
industry and government it is crucial for predictive models to be
uncertainty-aware and yield trustworthy predictions. Another key requirement
for deployment of AI at enterprise scale is to realize the importance of
integrating human-centered design into AI systems such that humans are able to
use systems effectively, understand results and output, and explain findings to
oversight committees.
While the focus of this symposium was on AI systems to improve data quality
and technical robustness and safety, we welcomed submissions from broadly
defined areas also discussing approaches addressing requirements such as
explainable models, human trust and ethical aspects of AI.

研究旨在提高人工智能系统的可信度和信任度，开发能够适应实际应用场景的人工智能系统，并将人类放在其中心地位，使其能够有效地使用系统，理解输出和解释结果以满足监管委员会的要求。