Towards Accountability in Machine Learning Applications: A System-testing Approach

Wan, Wayne Xinwei; Lindenthal, Thies

doi:10.2139/ssrn.3758451

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…Next, ethical principles argue AI systems should be developed to do good or benefit someone or the society as a whole (beneficence); they should avoid doing harm to others (non-maleficence) [27,34]. Finally, rules should be established on managing conflict of interest situations within the team or when the values of the system conflict with the interests or values of the users [62,63].…”

Section: Management I Governancementioning

confidence: 99%

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Miller¹

2021

Annals of Computer Science and Information Systems

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The algorithms implemented through artificial intelligence (AI) and big data projects are used in life-anddeath situations. While research exists to address varying aspects of moral decision-making with algorithms, the definition of project success is not readily available. Nevertheless, researchers place the burden of responsibility for ethical decisions from AI systems on the system developers. Using a systematic literature review, this research identified 71 AI project success factors in 14 groups related to moral decision-making with algorithms. It contributes to project management literature, specifically for AI projects. Project managers and sponsors can use the results during project planning and execution.

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Section: Management I Governancementioning

confidence: 99%

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Miller¹

2021

Annals of Computer Science and Information Systems

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Artificial Intelligence Project Success Factors—Beyond the Ethical Principles

Miller¹

2022

Lecture Notes in Business Information Processing

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The algorithms implemented through artificial intelligence (AI) and big data projects are used in life-and-death situations. Despite research that addresses varying aspects of moral decision-making based upon algorithms, the definition of project success is less clear. Nevertheless, researchers place the burden of responsibility for ethical decisions on the developers of AI systems. This study used a systematic literature review to identify five categories of AI project success factors in 17 groups related to moral decision-making with algorithms. It translates AI ethical principles into practical project deliverables and actions that underpin the success of AI projects. It considers success over time by investigating the development, usage, and consequences of moral decision-making by algorithmic systems. Moreover, the review reveals and defines AI success factors within the project management literature. Project managers and sponsors can use the results during project planning and execution.

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Stakeholder-accountability model for artificial intelligence projects

Miller¹

2022

JEM

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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. Keywords: accountability, artificial intelligence, algorithms, project management, ethics. JEL Classification: C33, M15, O3, O32, O33, Q55.

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Towards Accountability in Machine Learning Applications: A System-testing Approach

Cited by 3 publications

References 53 publications

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Artificial Intelligence Project Success Factors—Beyond the Ethical Principles

Stakeholder-accountability model for artificial intelligence projects

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