To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems

2026-04-30

AI Summary

This study addresses the limited systematic understanding of why AI systems are abandoned or never developed prior to deployment—a gap that hinders effective early-stage interventions in responsible AI research. Employing a mixed-methods approach comprising a scoping review, case database mining, and practitioner surveys, the work proposes the first comprehensive framework categorizing drivers of AI project termination across six dimensions: ethical, organizational, resource-related, regulatory, technical, and market factors. Empirical findings reveal that reasons for halting AI initiatives are highly heterogeneous, with non-ethical considerations often predominating in practice. This framework offers novel directions for early intervention and actionable strategies to support responsible AI development from inception.

Abstract

Responsible AI research typically focuses on examining the use and impacts of deployed AI systems. Yet, there is currently limited visibility into the pre-deployment decisions to pursue building such systems in the first place. Decisions taken in the earlier stages of development shape which systems are ultimately released, and therefore represent potential, but underexplored, points for intervention. As such, this paper investigates factors influencing AI non-development and abandonment throughout the development lifecycle. Specifically, we first perform a scoping review of academic literature, civil society resources, and grey literature including journalism and industry reports. Through thematic analysis of these sources, we develop a taxonomy of six categories of factors contributing to AI abandonment: ethical concerns, stakeholder feedback, development lifecycle challenges, organizational dynamics, resource constraints, and legal/regulatory concerns. Then, we collect data on real-world case of AI system abandonment via an AI incident database and a practitioner survey to evidence and compare factors that drive abandonment both prior to and following system deployment. While academic responsible AI communities often emphasize ethical risks as reasons to not develop AI, our empirical analysis of these cases demonstrates the diverse, and often non-ethics-related, levers that motivate organizations to abandon AI development. Synthesizing evidence from our taxonomy and related case study analyses, we identify gaps and opportunities in current responsible AI research to (1) engage with the diverse range of levers that influence organizations to abandon AI development, and (2) better support appropriate (dis)engagement with AI system development.

Problem

• AI abandonment
• non-development
• responsible AI
• pre-deployment decisions
• development lifecycle

Innovation

• AI abandonment
• responsible AI
• development lifecycle
• non-development decisions
• thematic taxonomy