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.

翻译：负责任的AI研究通常关注已部署AI系统的使用和影响。然而，对于最初决定是否构建此类系统的预部署决策，目前缺乏可见性。开发早期阶段所做的决策决定了最终哪些系统会被发布，因此代表了潜在但尚未充分探索的干预点。有鉴于此，本文研究了整个开发生命周期中影响AI未开发和弃用的因素。具体而言，我们首先对学术文献、民间社会资源以及包括新闻和行业报告在内的灰色文献进行了范围综述。通过对这些来源的主题分析，我们建立了一个包含六类导致AI弃用因素的分类法：伦理关切、利益相关者反馈、开发生命周期挑战、组织动态、资源约束以及法律/监管关切。随后，我们通过AI事件数据库和实践者调查收集了现实世界中AI系统弃用的案例数据，以证明并比较系统部署前后导致弃用的因素。尽管负责任的AI学术界经常强调伦理风险是不开发AI的理由，但我们对此类案例的实证分析表明，促使组织放弃AI开发的杠杆是多样化的，且往往与伦理无关。综合来自我们分类法和相关案例研究分析的证据，我们识别出当前负责任的AI研究中的空白与机遇，以便（1）关注影响组织放弃AI开发的多样化杠杆，以及（2）更好地支持对AI系统开发的适当（不）参与。