Stakeholder-accountability model for artificial intelligence projects

Abstract

Abstract Aim/purpose – This research presents a conceptual stakeholder accountability model for mapping the project actors to the conduct for which they should be held accountable in artificial intelligence (AI) projects. AI projects differ from other projects in important ways, including in their capacity to inflict harm and impact human and civil rights on a global scale. The in-project decisions are high stakes, and it is critical who decides the system’s features. Even well-designed AI systems can be deployed in ways that harm individuals, local communities, and society. Design/methodology/approach – The present study uses a systematic literature review, accountability theory, and AI success factors to elaborate on the relationships between AI project actors and stakeholders. The literature review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement process. Bovens’ accountability model and AI success factors are employed as a basis for the coding framework in the thematic analysis. The study uses a web-based survey to collect data from respondents in the United States and Germany employing statistical analysis to assess public opinion on AI fairness, sustainability, and accountability. Findings – The AI stakeholder accountability model specifies the complex relationships between 16 actors and 22 stakeholder forums using 78 AI success factors to define the conduct and the obligations and consequences that characterize those relationships. The survey analysis suggests that more than 80% of the public thinks AI development should be fair and sustainable, and it sees the government and development organizations as most accountable in this regard. There are some differences between the United States and Germany regarding fairness, sustainability, and accountability. Research implications/limitations – The results should benefit project managers and project sponsors in stakeholder identification and resource assignment. The definitions offer policy advisors insights for updating AI governance practices. The model presented here is conceptual and has not been validated using real-world projects. Originality/value/contribution – The study adds context-specific information on AI to the project management literature. It defines project actors as moral agents and provides a model for mapping the accountability of project actors to stakeholder expectations and system impacts.