Broadening AI Ethics Narratives: An Indic Art View

TL;DR

This paper explores how Indian arts and cultural narratives can broaden AI ethics by uncovering unique socio-cultural perspectives, emphasizing empathy, multimodal data, and dynamic, lifelong learning approaches.

Contribution

It introduces a novel interdisciplinary approach by integrating Indian art traditions into AI ethics, highlighting non-Western perspectives and participatory practices to expand ethical frameworks.

Findings

Indian arts offer unique socio-cultural insights for AI ethics.

Incorporating empathy and multimodal data enhances AI ethical systems.

AI ethics should be dynamic, adaptable, and involve lifelong learning.

Abstract

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