From Values to Frameworks: A Qualitative Study of Ethical Reasoning in Agentic AI Practitioners

TL;DR

This study explores how AI practitioners ethically reason about autonomous systems, revealing three distinct frameworks—customer-centric, design-centric, and ethics-centric—that influence decision-making and ethical trade-offs.

Contribution

It identifies and characterizes three unique reasoning frameworks used by practitioners, advancing understanding of ethical decision processes in agentic AI development.

Findings

Practitioners use three main reasoning frameworks: customer-centric, design-centric, ethics-centric.

Frameworks influence how ethical trade-offs are navigated in autonomous AI systems.

Recognizing these frameworks can improve ethical oversight and decision-making in AI deployment.

Abstract

Agentic artificial intelligence systems are autonomous technologies capable of pursuing complex goals with minimal human oversight and are rapidly emerging as the next frontier in AI. While these systems promise major gains in productivity, they also raise new ethical challenges. Prior research has examined how different populations prioritize Responsible AI values, yet little is known about how practitioners actually reason through the trade-offs inherent in designing these autonomous systems. This paper investigates the ethical reasoning of AI practitioners through qualitative interviews centered on structured dilemmas in agentic AI deployment. We find that the responses of practitioners do not merely reflect value preferences but rather align with three distinct reasoning frameworks. First is a Customer-Centric framework where choices are justified by business interests, legality, and user autonomy. Second is a Design-Centric framework emphasizing technical safeguards and system constraints. Third is an Ethics-Centric framework prioritizing social good and moral responsibility beyond compliance. We argue that these frameworks offer distinct and necessary insights for navigating ethical trade-offs. Consequently, providers of agentic AI must look beyond general principles and actively manage how these diverse reasoning frameworks are represented in their decision-making processes to ensure robust ethical outcomes.