Towards Effective Human Intervention in Algorithmic Decision-Making
Understanding the Effect of Decision-Makers' Configuration on Decision-Subjects' Fairness Perceptions
Mireia Yurrita (TU Delft - Perceptual Intelligence)
Himanshu Verma (TU Delft - Human-Centred Artificial Intelligence)
Agathe Balayn (TU Delft - Organisation & Governance)
Ujwal Gadiraju (TU Delft - Web Information Systems)
Sylvia C. Pont (TU Delft - Perceptual Intelligence)
Alessandro Bozzon (TU Delft - Human-Centred Artificial Intelligence)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Human intervention is claimed to safeguard decision-subjects’ rights in algorithmic decision-making and contribute to their fairness perceptions. However, how decision-subjects perceive hybrid decision-maker configurations (i.e., combining humans and algorithms) is unclear. We address this gap through a mixed-methods study in an algorithmic policy enforcement context. Through qualitative interviews (Study 1; N1 = 21), we identify three characteristics (i.e., decision-maker’s profile, model type, input data provenance) that affect how decision-subjects perceive decision-makers’ ability, benevolence, and integrity (ABI). Through a quantitative study (Study 2; N2 = 223), we then systematically evaluate the individual and combined effects of these characteristics on decision-subjects’ perceptions towards decision-makers, and fairness perceptions. We found that only decision-maker’s profile contributes to perceived ability, benevolence, and integrity. Interestingly, the effect of decision-maker’s profile on fairness perceptions was mediated by perceived ability and integrity. Our findings have design implications for ensuring effective human intervention as a protection against harmful algorithmic decisions.
help_outline