Investigating the Impact of Rewards and Sanctions on Developers’ Proactive AI Accountability Behavior

Abstract

Accountability of artificial intelligence (AI)-based systems is often addressed reactively, mainly after harm occurs. This study shifts toward proactive approaches, highlighting AI developers’ role in risk mitigation. Proactive AI accountability behavior refers to self-initiated, future-oriented actions that go beyond formal job roles to justify developers’ actions and decisions, and to facilitate the clear attribution of accountability. Drawing on Proactive Motivation Theory, we conducted an online experiment (n = 264) to investigate how governance mechanisms (rewards vs. sanctions) and motivational states impact such behavior. Our results reveal flexible role orientation as the key driver of proactive behavior and how rewards and sanctions impact such a mindset. We contribute by conceptualizing proactive AI accountability behavior and providing a theoretical model that explains its emergence, underscoring the importance of using rewards to foster a proactive mindset alongside sanctions as guardrails against harmful initiatives.