To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
In the rapidly evolving landscape of artificial intelligence (AI), the focus of responsible AI research has predominantly centered on the ramifications of deployed systems. However, a significant gap exists in understanding the pre-deployment decision-making processes that determine whether to pursue the development of AI systems at all. Recognizing the importance of these initial decisions is crucial, as they lay the groundwork for which systems ultimately make it to market. This article delves into the intricate factors influencing the non-development and abandonment of AI systems, as explored in the recent paper published on arXiv:2604.28053v1.
The paper begins with a comprehensive scoping review, drawing insights from a diverse array of sources, including academic literature, civil society resources, and grey literature such as journalism and industry reports. Through thematic analysis, the authors develop a taxonomy that categorizes six primary factors contributing to the abandonment of AI projects:
- Ethical Concerns: Many organizations abandon AI development due to potential ethical implications. These concerns often stem from worries about bias, fairness, and the long-term societal impacts of AI technologies.
- Stakeholder Feedback: Input from stakeholders—including users, clients, and community members—can significantly influence decisions regarding the continuation of AI projects. Negative feedback or concerns can prompt organizations to halt development.
- Development Lifecycle Challenges: Complexities encountered during the development lifecycle, such as technical obstacles and unforeseen complications, can lead organizations to reconsider their commitment to an AI project.
- Organizational Dynamics: Internal factors, including company culture, leadership changes, and strategic shifts, can affect the prioritization of AI initiatives, sometimes resulting in abandonment.
- Resource Constraints: Development often requires substantial financial and human resources. Limited availability of these resources can lead organizations to abandon AI projects, particularly if expected returns are deemed insufficient.
- Legal/Regulatory Concerns: The evolving landscape of laws and regulations surrounding AI can create uncertainty, leading organizations to pause or abandon projects to avoid potential legal pitfalls.
To substantiate their findings, the authors collected data from an AI incident database and conducted a practitioner survey, focusing on real-world cases of AI system abandonment. This empirical analysis highlighted a diverse range of factors influencing decisions to abandon AI development, many of which extend beyond ethical considerations. For instance, while ethical risks are frequently emphasized in discussions about responsible AI, the study reveals that non-ethics-related levers often play a more substantial role in driving organizations to cease development.
By synthesizing evidence from their taxonomy and related case studies, the authors identify critical gaps and opportunities within current responsible AI research. They argue for a broader engagement with the diverse range of factors influencing organizational decisions regarding AI development, emphasizing the need to better support appropriate (dis)engagement with AI system creation.
This exploration into the non-development and abandonment of AI systems highlights the multifaceted nature of decision-making in AI development. As organizations navigate the complex landscape of ethical, technical, and regulatory considerations, understanding these factors will be pivotal in fostering responsible AI practices and ensuring that the systems that do reach deployment are both effective and ethically sound.
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