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Established contributors are the backbone of many free/libre open source software (FLOSS) projects. Previous research has shown that it is critically important for projects to retain contributors and it has also revealed the motivations behind why contributors choose to participate in FLOSS in the first place. However, there has been limited research done on the reasons why established contributors disengage, and factors (on an individual and project level) that predict their disengagement. In this paper, we conduct a mixed-methods empirical study, combining surveys and survival modeling, to identify the reasons and predictive factors behind established contributor disengagement. We find that different groups of established contributors tend to disengage for different reasons; however, overall contributors most commonly cite some kind of transition (e.g., switching jobs or leaving academia). We also find that factors such as the popularity of the projects a contributor works on, whether they have experienced a transition, when they work, and how much they work are all factors that can be used to predict their disengagement from open source.

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Responsible artificial intelligence guidelines ask engineers to consider how their systems might harm. However, contemporary artificial intelligence systems are built by composing many preexisting software modules that pass through many hands before becoming a finished product or service. How does this shape responsible artificial intelligence practice? In interviews with 27 artificial intelligence engineers across industry, open source, and academia, our participants often did not see the questions posed in responsible artificial intelligence guidelines to be within their agency, capability, or responsibility to address. We use Suchman's “located accountability” to show how responsible artificial intelligence labor is currently organized and to explore how it could be done differently. We identify cross-cutting social logics, like modularizability, scale, reputation, and customer orientation, that organize which responsible artificial intelligence actions do take place and which are relegated to low status staff or believed to be the work of the next or previous person in the imagined “supply chain.” We argue that current responsible artificial intelligence interventions, like ethics checklists and guidelines that assume panoptical knowledge and control over systems, could be improved by taking a located accountability approach, recognizing where relations and obligations might intertwine inside and outside of this supply chain.

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