Participatory Approaches in AI Development and Governance: Case Studies

TL;DR

This paper examines participatory approaches in AI development and governance through case studies in facial recognition and healthcare LLMs, highlighting practical implementation challenges and benefits.

Contribution

It provides empirical insights into applying participatory AI methods in contentious and innovative sectors, illustrating how stakeholder engagement can improve trust and system quality.

Findings

Participatory methods enhance stakeholder trust and system transparency.

Implementation depends on stakeholder identification and information integration.

Case studies reveal sector-specific challenges and opportunities.

Abstract

This paper forms the second of a two-part series on the value of a participatory approach to AI development and deployment. The first paper had crafted a principled, as well as pragmatic, justification for deploying participatory methods in these two exercises (that is, development and deployment of AI). The pragmatic justification is that it improves the quality of the overall algorithm by providing more granular and minute information. The more principled justification is that it offers a voice to those who are going to be affected by the deployment of the algorithm, and through engagement attempts to build trust and buy-in for an AI system. By a participatory approach, we mean including various stakeholders (defined a certain way) in the actual decision making process through the life cycle of an AI system. Despite the justifications offered above, actual implementation depends crucially on how stakeholders in the entire process are identified, what information is elicited from them, and how it is incorporated. This paper will test these preliminary conclusions in two sectors, the use of facial recognition technology in the upkeep of law and order and the use of large language models in the healthcare sector. These sectors have been chosen for two primary reasons. Since Facial Recognition Technologies are a branch of AI solutions that are well-researched and the impact of which is well documented, it provides an established space to illustrate the various aspects of adapting PAI to an existing domain, especially one that has been quite contentious in the recent past. LLMs in healthcare provide a canvas for a relatively less explored space, and helps us illustrate how one could possibly envision enshrining the principles of PAI for a relatively new technology, in a space where innovation must always align with patient welfare.