Let the user have a say - voice in automated decision-making

Hellwig, Paul; Buchholz, Victoria; Kopp, Stefan; Maier, Günter W.

doi:10.1016/j.chb.2022.107446

Cited by 11 publications

(3 citation statements)

References 49 publications

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“…To address the power imbalance between platform workers and companies, the first step should involve increasing transparency and explainability within the platform economy (Rani and Furrer, 2021). Additionally, outcome control (in addition to outcome explanations) improves perceived justice by allowing people a voice and bringing human elements into final decisions (Hellwig et al, 2023; Lee et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Understanding justice in the platform economy: A qualitative case study of platform-based food delivery work

Lee

2024

Journal of Industrial Relations

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This qualitative study explores the conceptualisation of justice in the context of platform work, focusing on the dimensions of distributive, procedural, interpersonal, and informational justice. Drawing upon in-depth interviews with platform-based food delivery couriers in Taiwan, the study extends the organisational justice theory to platform work, showing how the dimensions of justice remain highly relevant in platform work, despite its individualised work organisation and algorithm-mediated transactions. The study highlights the influence of legal categorisations on distributive justice perceptions, while uncovering how perceptions of procedural justice had been undermined in relation to task allocation, fee calculations, and the lack of voice and consultation. Furthermore, it underscores the significance of dignity, respect, transparency, and explainability in interpersonal and informational justice within the platform work context. The theoretical and empirical contributions provide insights for scholars, practitioners, and policymakers seeking to better understand and address justice concerns within the rapidly expanding platform work sector.

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Section: Discussionmentioning

confidence: 99%

Understanding justice in the platform economy: A qualitative case study of platform-based food delivery work

Lee

2024

Journal of Industrial Relations

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“…The robot played only an advisory role. The final decision was made by the subjects themselves, as voice influences the fairness assessment of algorithm-based decision systems (Hellwig et al, 2023), and algorithm aversion should not be unnecessarily exacerbated (Burton et al, 2020;Bigman and Gray, 2018).…”

Section: The Recommendationsmentioning

confidence: 99%

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

Arlinghaus,

Strassmann,

Dix

2024

Preprint

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This German study (N = 317) tests social communication (i.e., self-disclosure, content intimacy, relational continuity units, we-phrases) as a potential compensation strategy for algorithm aversion. Therefore, we explore the acceptance of a robot as an advisor in non-moral, somewhat moral, and very moral decision situations and compare the influence of two verbal communication styles of the robot (functional vs. social). Subjects followed the robot’s recommendation similarly often for both communication styles (functional vs. social), but more often in the non-moral decision situation than in the moral decision situations. Subjects perceived the robot as more human and more moral during social communication than during functional communication but similarly trustworthy, likable, and intelligent for both communication styles. In moral decision situations, subjects ascribed more anthropomorphism and morality but less trust, likability, and intelligence to the robot compared to the non-moral decision situation. Subjects perceive the robot as more moral in social communication. This unexpectedly led to subjects following the robot’s recommendation less often. No other mediation effects were found. From this we conclude, that the verbal communication style alone has a rather small influence on the robot’s acceptance as an advisor for moral decision-making and does not reduce algorithm aversion. Potential reasons for this (e.g., multimodality, no visual changes), as well as implications (e.g., avoidance of self-disclosure in human-robot interaction) and limitations (e.g., video interaction) of this study, are discussed.

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“…Given the significant number of studies that have examined the perceived fairness of AI-based decisions (for overviews see Langer & Landers, 2021;Starke et al, 2022), these studies could also provide intuition as to what factors might influence people's perceived fairness and thus their response bias in detecting unfairness. To name just a few, the context of use (Lee, 2018), the stakes of the decisions (Langer et al, 2019), feature properties (e.g., their relevance, reliability; Grgić-Hlača et al, 2018; how sensitivity features are; Nyarko et al, 2021), the possibility to appeal algorithmic decisions (Hellwig et al, 2023), explanations (Lee et al, 2019;Newman et al, 2020;Schlicker et al, 2021), explanation styles (Binns et al, 2018;Dodge et al, 2019), and visualization techniques (Van Berkel et al, 2021) have been shown to affect the perceived fairness of system decisions and future research could test whether these effects translate into a change in response bias. It may also be interesting to examine whether perceived fairness mainly affects people's response bias or whether there is also a relationship with people's sensitivity.…”

Section: Response Biasmentioning

confidence: 99%

A signal detection perspective on error and unfairness detection as a critical aspect of human oversight of AI-based systems

Langer¹,

Baum²,

Schlicker³

2023

Preprint

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Legislation and ethical guidelines around the globe call for effective human oversight of the use of AI-based systems in high-risk contexts – that is oversight that reduces the risks associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., incorrect classifications) or to ethical concerns (e.g., unfairness of outputs). Given the significant role that human oversight is expected to play in the operation of AI-based systems, it is crucial to gain a better understanding of the conditions for effective oversight. In this paper, we build on the literature on the management of imperfect automation to show that the reliable detection of errors or other deviant behavior is crucial for the effective management of these errors and thus crucial for effective oversight. We then propose that Signal Detection Theory (SDT) offers a promising framework for better understanding what affects people’s sensitivity (i.e., how well they are able to detect errors) and response bias (i.e., the tendency to report errors given a perceived evidence of an error) in detecting errors. To demonstrate the broad applicability of an SDT perspective to the study of error detection when overseeing AI-based systems, we then explicate the specifics for the case of unfairness detection. Additionally, we propose factors (task-, system-, and person-related factors) that may affect the sensitivity and response bias of humans tasked with detecting unfairness associated with the use of AI-based systems. Finally, we discuss implications and future research directions for an SDT perspective on error detection.

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Let the user have a say - voice in automated decision-making

Cited by 11 publications

References 49 publications

Understanding justice in the platform economy: A qualitative case study of platform-based food delivery work

Understanding justice in the platform economy: A qualitative case study of platform-based food delivery work

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

A signal detection perspective on error and unfairness detection as a critical aspect of human oversight of AI-based systems

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