Understanding Decision Subjects' Needs and Perceptions Towards Contestable AI Systems

Contestability, i.e., a property that makes AI systems open to human intervention throughout their lifecycles, has been claimed to be essential for counteracting algorithmic harms. By enabling decision subjects to influence algorithmic outputs, contestable AI systems aim to safeguard decision subjects' rights to autonomy and dignity. Despite the interest and relevance of contestability in HCI, little is known about whether and how elements of contestable AI systems can empower decision subjects in algorithmic decision-making. In this dissertation, we aim to generate empirical insights into decision subjects' needs for and fairness perceptions towards contestable AI systems in decision-making. By focusing on decision subjects, this dissertation leads to a set of recommendations for organizations setting up algorithmic decision-making processes. These recommendations encourage organizations to account for the interests of those impacted by algorithmic decisions from the early stages of the design process.