Trust in an AI versus a Human teammate: The effects of teammate identity and performance on Human-AI cooperation

Zhang, Guanglu; Chong, Leah; Kotovsky, Kenneth; Cagan, Jonathan

doi:10.1016/j.chb.2022.107536

Cited by 30 publications

(11 citation statements)

References 30 publications

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“…These hallucinations are challenging to detect and correct because they are deceptive, often blending seamlessly with accurate information and requiring meticulous examination. Moreover, the study illustrates the profound impact of AI on human trust and decision making, particularly in high-stakes domains such as healthcare and law enforcement [2]. Inconsistent or erroneous AI performance can significantly erode human trust, undermining the integrity and reliability of AI systems in areas where precision and reliability are paramount.…”

Section: Limitationsmentioning

confidence: 95%

“…The authors of [2] delve into the nuanced relationship between human trust and AI performance, a research area particularly relevant in fields such as healthcare, law enforcement, and autonomous systems, where the role of AI is increasingly prominent. This study highlights how inconsistent or erroneous AI performance can significantly erode human trust, a critical issue in high-stakes domains where AI recommendations or decisions may be integral to operations.…”

Section: Addressing the Challenge Of Ai Hallucinations In High-stakes...mentioning

confidence: 99%

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The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

Williamson,

Prybutok

2024

Information

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This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges.

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Section: Limitationsmentioning

confidence: 95%

Section: Addressing the Challenge Of Ai Hallucinations In High-stakes...mentioning

confidence: 99%

The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

Williamson,

Prybutok

2024

Information

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“…As noted above, considerable attention is given to the idea that AI can be a full-fledged teammate (Seeber et al, 2020; G. Zhang et al, 2023), yet researchers differ in their assessments of how realistic the notion of an AI teammate is. Some suggest that human–AI teamwork is our current reality (L. Larson & DeChurch, 2020; McNeese et al, 2021), whereas others argue that AI still lacks the full situational and social awareness necessary for teamwork with humans (Beans, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Theoretical Propositionsmentioning

confidence: 99%

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

Bezrukova

Spell

Rice

et al. 2023

Group & Organization Management

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We theorize about human-team collaboration with AI by drawing upon research in groups and teams, social psychology, information systems, engineering, and beyond. Based on our review, we focus on two main issues in the teams and AI arena. The first is whether the team generally views AI positively or negatively. The second is whether the decision to use AI is left up to the team members (voluntary use of AI) or mandated by top management or other policy-setters in the organization. These two aspects guide our creation of a team-level conceptual framework modeling how AI introduced as a mandated addition to the team can have asymmetric effects on collaboration level depending on the team’s attitudes about AI. When AI is viewed positively by the team, the effect of mandatory use suppresses collaboration in the team. But when a team has negative attitudes toward AI, mandatory use elevates team collaboration. Our model emphasizes the need for managing team attitudes and discretion strategies and promoting new research directions regarding AI’s implications for teamwork.

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“…Researchers have explored metrics for measuring the trust a human has for a robot. 1,2 As autonomous agents become more capable of complex decision-making, there is an need for autonomous agents to learn to share information, rely on, and collaborate with their human teammates. In many human-robot teams, the robot is considered more of a tool, than a team member working with the human.…”

Section: Introductionmentioning

confidence: 99%

Human-robot teaming for a cooperative game in a shared partially observable space

Mooers,

Aldridge,

Buck

et al. 2023

Geospatial Informatics XIII

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An open research question is how to best pair a human and agent (e.g., AI, autonomous) relative to a complex, multi-objective task in a dynamic and unknown partially observable environment. At the heart of this challenge resides even deeper questions like what AI is needed and how can bi-directional and multi-directional human-robot trust be established. In this paper, the theoretical framework for a simple 2D grid world-based cooperative search and rescue game is explored. The resultant prototype interface enables the study of human-robot interaction for human-robot teaming. First, the design and implementation of a prototype interface is discussed. A 2D grid-world was selected to simplify the investigation and eliminate confounding factors that arise in more complicated simulated 3D and real world experiments. Next, different types of autonomous agents are introduced, as they impact our studies and ultimately are an integral element of the underlying research question. This is followed by three levels of increasing complexity open-ended games, easy, medium, and hard. The current paper does not contain human experimentation results. That is the next step in this research. Instead, this article introduces, explains, and defends a set of design choices and working examples are provided to facilitate open discussion.

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Trust in an AI versus a Human teammate: The effects of teammate identity and performance on Human-AI cooperation

Cited by 30 publications

References 30 publications

The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

Human-robot teaming for a cooperative game in a shared partially observable space

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