Supradyadic Trust in Artificial Intelligence

Dorton, Stephen L.

doi:10.54941/ahfe1001451

Cited by 3 publications

(8 citation statements)

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“…We found that trust could be lost for a wide variety of stakeholders (users, developers, deployers, etc.). These findings align somewhat with the concept of supradydadic trust (i.e., trust outside of the user-AI dyad; Dorton, 2022). They also align with recent research on system-wide trust, or the idea that different components within a sociotechnical system (AI, people, work, etc.)…”

Section: Other Insightssupporting

confidence: 86%

“…The AI Incident Database allowed us to answer various research questions about how trust in AI is lost “in the wild” across various contexts. To be more precise, we found the AI Incident Database to be a viable means to conduct naturalistic research, although it appears to have advantages and disadvantages when compared to interview-based knowledge elicitation methods such as the critical incident technique (e.g., Dorton & Harper, 2022a; Dorton, 2022):…”

Section: Discussionmentioning

confidence: 99%

“…The Naturalistic Decision Making (NDM) tradition focuses on developing understanding of human cognition situated in the context of work (vs in a controlled lab setting) and enables the engineering of systems to support cognitive processes (Klein et al, 1993). Naturalistic inquiry has been effectively applied to glean insights on how trust is formed in AI in high consequence work (Rotner et al, 2021; Dorton & Harper, 2022a; Dorton, 2022), work adaptations to changes in trust (Dorton et al, 2022), and log-term trust dynamics with AI (Dorton & Harper, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Exploring Trust With the AI Incident Database

Stanley,

Dorton

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Engineering trustworthy artificial intelligence (AI) is important to adoption and appropriate use, but there are challenges to implementing trustworthy AI systems. It is difficult to translate trust studies from the laboratory to the field. It is also difficult to operationalize “trustworthy AI” frameworks and principles to inform the actual development of AI. We address these challenges with an approach based in reported incidents of trust loss “in the wild.” We systematically identified 30 cases of trust loss in the AI Incident Database to gain insight into how and why humans lose trust in AI in various contexts. These factors could be codified into the development cycle in various forms such as checklists and design patterns to manage trust in AI systems and avoid similar incidents in the future. Because it is based in real incidents, this approach offers recommendations that are concrete and actionable for teams addressing real use cases with AI systems.

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Section: Other Insightssupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Trust With the AI Incident Database

Stanley,

Dorton

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Premortems do not guarantee an exhaustive set of risks will be identified (Bettin et al, 2022 ). Similarly, methods such as analytic games (e.g., de Rosa and De Gloria, 2021 ) and other checklists based on naturalistic inquiry (e.g., Dorton, 2022 ) are bounded in scope by not only the scenarios and injects examined, but also the expertise of the participants involved in their application. More generally, there is a long-standing challenge of developing tools for an envisioned world with technological change (e.g., Woods and Dekker, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recent work has shown that analytic wargames involving experienced players in naturalistic settings can be used to test disruptive technologies and uncover emergent behaviors (de Rosa and De Gloria, 2021 ), and to explore how the work system may stretch with the introduction of new technologies (Dorton et al, 2020 ). Other approaches have included the use of naturalistic methods such as the critical incident technique to develop evidence-based checklists for AI developers (e.g., Dorton, 2022 ). Further still, new methods based on naturalistic inquiry such as Systematic Contributors and Adaptation Diagramming (SCAD; Jefferies et al, 2022 ) and Joint Activity Monitoring (JAM; Morey et al, 2022 ) have been developed to attempt to identify issues in work systems more proactively.…”

Section: Increasing Understanding and Foresightmentioning

confidence: 99%

Foresight for ethical AI

Dorton

Ministero

Alaybek

et al. 2023

Front. Artif. Intell.

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There is growing expectation that artificial intelligence (AI) developers foresee and mitigate harms that might result from their creations; however, this is exceptionally difficult given the prevalence of emergent behaviors that occur when integrating AI into complex sociotechnical systems. We argue that Naturalistic Decision Making (NDM) principles, models, and tools are well-suited to tackling this challenge. Already applied in high-consequence domains, NDM tools such as the premortem, and others, have been shown to uncover a reasonable set of risks of underlying factors that would lead to ethical harms. Such NDM tools have already been used to develop AI that is more trustworthy and resilient, and can help avoid unintended consequences of AI built with noble intentions. We present predictive policing algorithms as a use case, highlighting various factors that led to ethical harms and how NDM tools could help foresee and mitigate such harms.

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Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

Bostrom,

Demuth,

Wirz

et al. 2023

Risk Analysis

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Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human–AI teaming perspectives on AI development similarly underscore. Co‐development strategies may also help reconcile efforts to develop performance‐based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

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Supradyadic Trust in Artificial Intelligence

Cited by 3 publications

References 0 publications

Exploring Trust With the AI Incident Database

Exploring Trust With the AI Incident Database

Foresight for ethical AI

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

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