Broadening AI Ethics Narratives: An Indic Art View
- URL: http://arxiv.org/abs/2204.03789v5
- Date: Mon, 15 May 2023 20:18:14 GMT
- Title: Broadening AI Ethics Narratives: An Indic Art View
- Authors: Ajay Divakaran and Aparna Sridhar and Ramya Srinivasan
- Abstract summary: The field of arts is perceived to play a key role in elucidating diverse historical and cultural narratives.
Most of the works that examine the interplay between the field of arts and AI ethics concern digital artworks.
We explore how it non-Western ethical abstractions, methods of learning, and participatory practices can shed light on aspects related to ethical AI systems.
- Score: 8.821884881723285
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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
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