Abstract
Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring
Highlights
The process of decision-making is prone to a diverse range of biases that can lead, at least in certain contexts, to erroneous judgments or disadvantageous choices (e.g., [1,2,3,4])
This article aims to identify peoples’ attitudes towards Algorithmic decision-making (ADM) systems and ensuing behaviours when dealing with ADM systems as identified in the literature and in relation to credit scoring
Different attitudes and behaviours are formed while using ADM systems that we have described in chapter two: trust, complacency, automation bias, and algorithmic aversion
Summary
The process of decision-making is prone to a diverse range of biases that can lead, at least in certain contexts, to erroneous judgments or disadvantageous choices (e.g., [1,2,3,4]). Systems that enable inanimate objects to act and decide to some extent on their own In this context, the algorithms, for example, autonomous cars or robots, are making decisions on behalf of the users. We will be concerned with ADM systems giving recommendations to support the decision-making process of a user or operator, irrespective of their influence on the decision outcome. Algorithms can only be useful to support human decision-making if users, operators, and stakeholders trust them [50]. Inappropriate reliance resulting in disuse and misuse of automation is frequently caused by a mismatch between the system’s capabilities and the trust invested This discrepancy is described in terms of (i) calibration, (ii) overtrust, and (iii) resolution [46]. The attention of participants adopting the algorithm was lower than non-adopters’, but it was still higher than baseline, meaning they did pay attention to what the algorithm was doing
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