Algorithmic Accountability: Designing Governance for Responsible Digital Transformations

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 investigates the affordances, responsibilities, and outcomes of algorithmic decision-making. To this end, we draw a framework for accountable algorithmic decision-making grounded in the literature on legitimacy, participation, and inclusion. Second, we systematically collect, map, and compare varying notions of algorithmic agency. Third, we 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 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.

Building on ongoing international discussions, and in continuation of the team’s previous work within the SAMANSVAR framework on fair labor on platforms and the gig economy, we want to address the socioeconomic effects of increasing adoption of artificial intelligence, algorithmic management, and smart automated systems, whereby increasing productivity gains for organisations are met with concerns about whether the speed of implementation can be matched with necessary levels of accountability and oversight. Our research shall result in an unifying framework for algorithmic accountability for algorithmic management which shall serve as a basis for organizations, regulators, and social communities to take actionable steps towards ensuring accountable algorithmic decision-making processes. 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 stakeholders. Third, we will develop actionable guidance towards creating accountable algorithmic decision-making, based on both explainable programming and comprehensible communication of decision-making rationales and data sources. As a practical deliverable, we shall 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. Finally, we will develop a user-friendly accountability enhancing tool, the ‘Framework for Algorithmic Accountability’. The framework will be tested on real-world settings of algorithmic decision-making processes and can be utilised by researchers, activists or lay Internet users to challenge algorithmic systems.