The People Versus the Algorithm: Stakeholders and AI Accountability

As artificial intelligence (AI) applications are used for a wide range of tasks, the question about who is responsible for detecting and remediating problems caused by AI applications remains disputed. We argue that responsibility attributions proposed by management scholars fail to enable a practical solution as two aspects are overlooked: the difficulty to design a complex algorithm that does not produce adverse outcomes, and the conflict of interest inherited in some AI applications by design as proprietors and users employ the application for different purposes. In this conceptual paper, we argue that effective accountability can only be delivered through solutions that enable stakeholders to employ their collective intelligence effectively in compiling problem reports and analyze problem patterns. This allows stakeholders, including governments, to hold providers of AI applications accountable, and ensure that appropriate corrections are carried out in a timely manner.