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Paper Type
Complete
Description
Along with advancements in Artificial Intelligence (AI) and organizations' ability to use AI systems in their operations and business, several ethical and moral issues emerge. In response to managing ethical AI issues, principles for Responsible AI (RAI) have been developed. However, beyond the availability of these principles, more insights are needed on how to operationalize RAI principles. In this paper, we provide empirical findings about actual RAI practices derived from interviews with experts in America and Europe. Drawing from these empirical insights, we make three contributions to RAI literature. First, we provide a synthesis of current practices for providing fair, explainable, and inclusive AI. Second, we provide an overview of roles and structures for governing RAI in organizations. Finally, we show how these practices can be integrated into different activities within AI workflows.
Paper Number
1217
Recommended Citation
Akbari Ghatar, Pouria; Pappas, Ilias; and Vassilakopoulou, Polyxeni, "Practices for Responsible AI: Findings from Interviews with Experts" (2023). AMCIS 2023 Proceedings. 4.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/4
Practices for Responsible AI: Findings from Interviews with Experts
Along with advancements in Artificial Intelligence (AI) and organizations' ability to use AI systems in their operations and business, several ethical and moral issues emerge. In response to managing ethical AI issues, principles for Responsible AI (RAI) have been developed. However, beyond the availability of these principles, more insights are needed on how to operationalize RAI principles. In this paper, we provide empirical findings about actual RAI practices derived from interviews with experts in America and Europe. Drawing from these empirical insights, we make three contributions to RAI literature. First, we provide a synthesis of current practices for providing fair, explainable, and inclusive AI. Second, we provide an overview of roles and structures for governing RAI in organizations. Finally, we show how these practices can be integrated into different activities within AI workflows.
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