Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development

Schaefer, Kristin E.; Perelman, Brandon S.; Rexwinkle, Joe T.; Canady, Jonroy; Neubauer, Catherine; Waytowich, Nicholas R.; Larkin, Gabriella Brick; Cox, Katherine; Geuss, Michael N.; Gremillion, Gregory M.; Metcalfe, J. Stanley; DeCostanza, Arwen H.; Marathe, Amar R.

doi:10.1007/978-3-030-77283-3_7

Cited by 5 publications

(9 citation statements)

References 51 publications

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“…The research subjects included a broad range of college students, military personnel, Amazon Mechanical Turk participants, and general community solicitations for participation in a wide variety of empirical studies. Experiments included controlled, quasi-experimental studies, field experiments, and computer simulations [22], [23], [24]. While the number of studies have included a broad range of interactions and environments, trust has been measured in equally diverse ways.…”

Section: Trust In Ai For Decision-makingmentioning

confidence: 99%

“…𝑃𝑟(𝐴) = 𝑝 𝐴 • 𝑃𝑟(𝐷|𝐴) + 𝑝 𝐷𝑖𝑠𝐴 • 𝑃𝑟(𝐷|𝐷𝑖𝑠𝐴) (24) In this study, by using previous research method by [58], [63], the values of implicit probabilities 𝑝 𝐴 𝑎𝑛𝑑 𝑝 𝐷𝑖𝑠𝐴 are determined by 𝑝 𝐴 = Pr(𝐴) and 𝑝 𝐷𝑖𝑠𝐴 = Pr(𝐷𝑖𝑠𝐴) . Pr(𝐴) and Pr(𝐷𝑖𝑠 𝐴) are observed probabilities from categorization tasks.…”

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

“…Pr(𝐴) and Pr(𝐷𝑖𝑠 𝐴) are observed probabilities from categorization tasks. Although this might involve subjective determination of these values, the Markov model in equation (24), becomes weighted average of Pr(𝐷|𝐴) and Pr(𝐷|𝐷𝑖𝑠𝐴), and will not match the agree/disagree and delegate condition probabilities (Busemeyer et al 2009).…”

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

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Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Preprint

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Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N=192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human-AI decision-making and ameliorate the understanding the interaction dynamics at the confluence of human factors and information features.

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Section: Trust In Ai For Decision-makingmentioning

confidence: 99%

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

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Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Preprint

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“…29 Schaefer et al from Army Research Labs (ARL) described in detail four areas of critical research relating to human-autonomy teaming at the tactical edge: enabling Soldiers to predict AI, quantifying Soldier understanding of AI, Soldier-guided AI adaptation, and characterizing Soldier-AI performance. 30 These areas directly relate to Hawk as described and intersect with the implications of an AI/ML system that can learn in real time.…”

Section: Human-machine Teamingmentioning

confidence: 99%

Tactical agility for AI-enabled multi-domain operations

Sturzinger¹,

George²,

Satyanarayanan³

2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

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Commanders must remain agile and adaptive in the future Artificial Intelligence (AI)-enabled multi-domain battlespace, where critical decisions are made at the tactical edge. Over-reliance on static, cloud-centric approaches to Machine Learning Operations (MLOps) compromises such agility and adaptability. These systems must operate in a dynamic threat environment, and learn to detect novel threats during operation. They must be able to perform this learning through the execution of tactical MLOps under austere and degraded conditions, especially limited wireless network bandwidth. In response to these requirements, this paper describes Hawk, a system that leverages edge proximity for rapid and iterative execution of the Observe stage of the Observe-Orient-Decide-Act loop. Central to this architecture is the use of tactical cloudlets. These mini data centers provide cloud-like computing resources without the communication latency to exascale data centers. Hawk enables a human to guide MLOps at low cognitive load, thus enabling an operational objective to be achieved at speed and scale while remaining usable and explainable.

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“…The research subjects included a broad range of college students, military personnel, Amazon Mechanical Turk participants, and general community solicitations who participated in a wide variety of empirical studies. Experiments included controlled, quasi-experimental studies, field experiments, and computer simulations [ 22 , 23 , 24 ]. While a number of studies have included a broad range of interactions and environments, trust has been measured in equally diverse ways.…”

Section: Introductionmentioning

confidence: 99%

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Entropy

View full text Add to dashboard Cite

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.

show abstract

Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development

Cited by 5 publications

References 51 publications

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Tactical agility for AI-enabled multi-domain operations

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

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