New Information Technology and Implicit Bias

Elsbach, Kimberly D.; Stigliani, Ileana

doi:10.5465/amp.2017.0079

Cited by 52 publications

(33 citation statements)

References 137 publications

(178 reference statements)

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“…K. Lee, 2018), our participants perceived the automated system to be less able and less flexible, but also perceived the integrity (associated with systems being less biased) of systems to be comparably stronger. Overall, these findings indicate that people have specific attitudes towards automated systems that affect their evaluations of trustworthiness of automated systems differently for different tasks (Elsbach & Stigliani, 2019). Those attitudes seem to lead to high overall levels of initial trustworthiness assessments for classical automation tasks (e.g., monitoring) that afford mechanical skills and where primary performance measures are effectivity and efficiency, and to comparably low initial trustworthiness assessments for tasks that involve consideration of ethical issues and decisions about individuals (e.g., personnel selection, performance evaluation; see also Nagtegaal, 2021).…”

Section: Trust In Automation Depends On the Use Contextmentioning

confidence: 76%

“…In our case, highlighting imperfection might have reduced assumptions of consistency, thus trust violations but also repair interventions had stronger effects because people more likely believed that system performance can vary. This interpretation will hopefully stimulate future research regarding the way in which humans perceive automated systems and uncover human attitudes, expectations or heuristics regarding automated systems (Elsbach & Stigliani, 2019).…”

Section: Highlighting Imperfection Reduces Assumptions Of Consistency?mentioning

confidence: 92%

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Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

Langer¹,

König²,

Back³

et al. 2021

Preprint

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Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to ethical standards and a trust repair intervention. Specifically, participants evaluated applicant preselection outcomes by either a human or an automated system across twelve personnel selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to a violation of ethical standards. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). Results showed that participants initially perceived automated systems to be less trustworthy, and had less intention to trust automated systems. Specifically, participants perceived systems to be less able, and flexible, but also less biased – a result that was sustained even in light of unfair bias. Furthermore, in regard to the automated system the trust violation and the trust repair intervention had weaker effects. Those effects were partly stronger when highlighting imperfection for the automated system. We conclude that it is crucial to investigate trust processes in relation to automated systems in ethically sensitive domains such as personnel selection as insights from classical areas of automation might not translate to application contexts where ethical standards are central to trust processes.

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Section: Trust In Automation Depends On the Use Contextmentioning

confidence: 76%

Section: Highlighting Imperfection Reduces Assumptions Of Consistency?mentioning

confidence: 92%

Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

Langer¹,

König²,

Back³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, in one stream of studies, scholars have focused on the "potential for actions that new technologies provide to users" (Leonardi & Vaast, 2017, p. 152), such as the idea that ICT gives people the opportunity to share knowledge online (Haas, Criscuolo, & George, 2015). Another stream of studies has focused on the direct psychological effects of ICT use on users, such as psychological gratification (e.g., fulfilment of needs for autonomy, relatedness, and competence; Cascio & Montealegre, 2016) or the cognitive biases induced by ICT (e.g., Clark, Robert, & Hampton, 2016;Elsbach & Stigliani, 2019).…”

Section: Introductionmentioning

confidence: 99%

How Does the Use of Information Communication Technology Affect Individuals? A Work Design Perspective

Wang

Liu

Parker

2020

ANNALS

180

167

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People design and use technology for work. In return, technology shapes work and people. As information communication technology (ICT) becomes ever more embedded in today's increasingly digital organizations, the nature of our jobs, and employees' work experiences, are strongly affected by ICT use. This cross-disciplinary review focuses on work design as a central explanatory vehicle for exploring how individual ICT usage influences employees' effectiveness and well-being. We evaluated 83 empirical studies. Results show that ICT use affects employees through shaping three key work design aspects: job demands, job autonomy, and relational aspects. To reconcile previous mixed findings on the effects of ICT use on individual workers, we identify two categories of factors that moderate the effects of ICT use on work design: usertechnology fit factors and social-technology fit factors. We consolidate the review findings into a comprehensive framework that delineates both the work design processes linking ICT use and employee outcomes and the moderating factors. The review fosters an intellectual conversation across different disciplines, including organizational behavior, management information systems, and computer-mediated communication. The findings and proposed framework help to guide future research and to design high quality work in the digital era.

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“…Innovativeness serves as a proxy for newness and, in the medical field, for the consideration of the latest scientific knowledge. Individuals associate innovative technology with success and advancement [ 30 ]. This association prompts individuals to evaluate new technology performance favorably [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Soellner

Koenigstorfer

2021

BMC Med Inform Decis Mak

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Background Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician. Methods Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects. Results The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases. Conclusion Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments.

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New Information Technology and Implicit Bias

Cited by 52 publications

References 137 publications

Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

How Does the Use of Information Communication Technology Affect Individuals? A Work Design Perspective

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

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