Participatory Approaches in AI Development and Governance: A Principled Approach

• URL: http://arxiv.org/abs/2407.13100v1
• Date: Mon, 3 Jun 2024 09:49:42 GMT
• Title: Participatory Approaches in AI Development and Governance: A Principled Approach
• Authors: Ambreesh Parthasarathy, Aditya Phalnikar, Ameen Jauhar, Dhruv Somayajula, Gokul S Krishnan, Balaraman Ravindran,
• Abstract summary: This paper forms the first part of a two-part series on participatory governance in AI. It advances the premise that a participatory approach is beneficial to building and using more responsible, safe, and human-centric AI systems.
• Score: 9.271573427680087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread adoption of Artificial Intelligence (AI) technologies in the public and private sectors has resulted in them significantly impacting the lives of people in new and unexpected ways. In this context, it becomes important to inquire how their design, development and deployment takes place. Upon this inquiry, it is seen that persons who will be impacted by the deployment of these systems have little to no say in how they are developed. Seeing this as a lacuna, this research study advances the premise that a participatory approach is beneficial (both practically and normatively) to building and using more responsible, safe, and human-centric AI systems. Normatively, it enhances the fairness of the process and empowers citizens in voicing concerns to systems that may heavily impact their lives. Practically, it provides developers with new avenues of information which will be beneficial to them in improving the quality of the AI algorithm. The paper advances this argument first, by describing the life cycle of an AI system; second, by identifying criteria which may be used to identify relevant stakeholders for a participatory exercise; and third, by mapping relevant stakeholders to different stages of AI lifecycle. This paper forms the first part of a two-part series on participatory governance in AI. The second paper will expand upon and concretise the principles developed in this paper and apply the same to actual use cases of AI systems.

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