AI and (self-learning) algorithms are increasingly used to support, accelerate and even replace human decision-making in various public and private arenas. Algorithms determine decisions in stock-trading and finance, fraud detection, scientific discovery, medical diagnostics and online match-making. Such decisions made by artificial intelligence systems are often implicit and invisible and they are linked to both intentional and unintentional consequences. This increasingly makes them objects of public concern and scrutiny.
Against this background, this project offers a business ethics perspective on how social, commercial, and political actors on both a local and global scale can ensure accountability in algorithmic decision-making processes. Gathering a group of international researchers with expertise in law, internet studies, information systems, and management research, the project will conduct a multi-method and multi-stakeholder investigation to develop a comprehensive framework of the affordances, responsibilities, and outcomes of algorithmic decision-making.
To this end, we will first draw a framework for accountable algorithmic decision-making grounded in the literature on legitimacy, participation, and inclusion. Second, we will systematically collect, map, and compare varying notions of algorithmic agency. Here, we will set a particular emphasis on the importance of a ‘co-constitution’ of algorithmic agency between organizations and their stakeholders. Third, we will develop actionable guidance towards creating accountable algorithmic decision-making processes, based on both explainable programming and comprehensible communication of decision-making rationales and data sources. Finally, as a practical deliverable, we will create a normative model for evaluating accountability in algorithmic decision-making processes, examining to what extent the algorithms are transparent, provide proper dispute channels, and enable public oversight.
