论文标题
扩大AI伦理叙事：一种指示艺术观点
Broadening AI Ethics Narratives: An Indic Art View
论文作者
论文摘要
结合跨学科观点被视为增强人工智能（AI）伦理的重要一步。在这方面，人们认为艺术领域在阐明多样化的历史和文化叙事方面发挥了关键作用，成为跨研究社区的桥梁。研究艺术领域与AI伦理学之间相互作用的大多数作品涉及数字艺术品，在很大程度上探索了计算工具在AI系统中表现出偏见的潜力。在本文中，我们研究了一个互补的方向，即揭示了嵌入人制艺术中独特的社会文化观点，而人造艺术中又可以在扩大AI伦理的地平线方面有价值。通过在16位艺术家，艺术学者和各种印度艺术形式的研究人员中进行的半结构化访谈，例如音乐，雕塑，绘画，绘画，地板绘画，舞蹈等。我们探索{\ It {\ It non-western}伦理抽象，学习方法，学习方法以及在印度艺术中所观察到的最古老的企业，是一种古老而又具有影响力的艺术品，这些方面是一种启发性的，可以使某种态度的态度来实现。通过有关印度舞蹈系统（即{\ it'Natyashastra'}的案例研究），我们分析了增强AI系统中道德规范的潜在途径。我们研究的见解概述了（1）在道德AI算法中纳入同理心的需求，（2）整合用于伦理AI系统设计和发展的多模式数据格式，（3）将AI伦理视为动态，多样，多样性，累积和共享的过程，而不是一种静态的，自我构成的框架（4），而不是为统一的框架（4），以较大的范围（4）问责制
Incorporating interdisciplinary perspectives is seen as an essential step towards enhancing artificial intelligence (AI) ethics. In this regard, the field of arts is perceived to play a key role in elucidating diverse historical and cultural narratives, serving as a bridge across research communities. Most of the works that examine the interplay between the field of arts and AI ethics concern digital artworks, largely exploring the potential of computational tools in being able to surface biases in AI systems. In this paper, we investigate a complementary direction--that of uncovering the unique socio-cultural perspectives embedded in human-made art, which in turn, can be valuable in expanding the horizon of AI ethics. Through semi-structured interviews across sixteen artists, art scholars, and researchers of diverse Indian art forms like music, sculpture, painting, floor drawings, dance, etc., we explore how {\it non-Western} ethical abstractions, methods of learning, and participatory practices observed in Indian arts, one of the most ancient yet perpetual and influential art traditions, can shed light on aspects related to ethical AI systems. Through a case study concerning the Indian dance system (i.e. the {\it `Natyashastra'}), we analyze potential pathways towards enhancing ethics in AI systems. Insights from our study outline the need for (1) incorporating empathy in ethical AI algorithms, (2) integrating multimodal data formats for ethical AI system design and development, (3) viewing AI ethics as a dynamic, diverse, cumulative, and shared process rather than as a static, self-contained framework to facilitate adaptability without annihilation of values (4) consistent life-long learning to enhance AI accountability
