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

TL;DR

This paper explores the various factors influencing the decision to abandon AI development, highlighting that ethical concerns are just one of many diverse reasons organizations choose not to proceed with AI projects.

Contribution

It provides a comprehensive taxonomy of six categories of factors affecting AI non-development and offers empirical evidence showing diverse levers beyond ethics that lead to abandonment.

Findings

Ethical concerns are not the primary reason for AI abandonment.

Organizational dynamics and resource constraints significantly influence abandonment.

Empirical data reveals diverse, non-ethics-related reasons for AI project discontinuation.

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.