Looping Minds: How Cognitive Science Exerts Influence on Its Findings

Strle, Toma

doi:10.7906/indecs.16.4.2

Cited by 2 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And although ADM systems promise to reduce biases of decisions about credit scores, and the systems have more data available and a bigger processing capacity, the models employed are still human-made, mostly not user-centred, and thus criticized as biased [16], perceived to be unfair to certain individuals and can lead to negative feedback loops. For instance, some groups of people, to circumvent a negative credit score, even "play [...] the credit score game" [25; p.346], finding strategies to improve their credit score (also to enable upward social mobility [78]) by producing positive data; for instance, by joining lending circles where people lend money to each other without interest to build credit [85] (see also [86][87][88] for similarly created loops within systems, rich with social interaction).…”

Section: Attitudes Towards Credit Scoring Algorithmsmentioning

confidence: 99%

Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring

Gsenger¹,

Strle²

2021

Interdisciplinary Description of Complex Systems

Self Cite

View full text Add to dashboard Cite

Algorithmic decision-making (ADM) systems increasingly take on crucial roles in our technology-driven society, making decisions, for instance, concerning employment, education, finances, and public services. This article aims to identify peoples' attitudes towards ADM systems and ensuing behaviours when dealing with ADM systems as identified in the literature and in relation to credit scoring. After briefly discussing main characteristics and types of ADM systems, we first consider trust, automation bias, automation complacency and algorithmic aversion as attitudes towards ADM systems. These factors result in various behaviours by users, operators, and managers. Second, we consider how these factors could affect attitudes towards and use of ADM systems within the context of credit scoring. Third, we describe some possible strategies to reduce aversion, bias, and complacency, and consider several ways in which trust could be increased in the context of credit scoring. Importantly, although many advantages in applying ADM systems to complex choice problems can be identified, using ADM systems should be approached with caree.g., the models ADM systems are based on are sometimes flawed, the data they gather to support or make decisions are easily biased, and the motives for their use unreflected upon or unethical.

show abstract

Section: Attitudes Towards Credit Scoring Algorithmsmentioning

confidence: 99%