The privileged sensing framework: A principled approach to improved human-autonomy integration

Marathe, Amar R.; Metcalfe, J. Stanley; Lance, Brent J.; Lukos, Jamie R.; Jangraw, David C.; Lai, Kevin; Touryan, Jonathan; Stump, Ethan; Sadler, Brian M.; Nothwang, William D.; McDowell, Kaleb

doi:10.1080/1463922x.2017.1297865

Cited by 14 publications

(5 citation statements)

References 80 publications

(94 reference statements)

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“…24,25 The military human performance research community plays a vital role in helping to make machines and AI more human-centric, built on the basis of an understanding of human biology. 26,27 If the human is the primary weapon system and all technology is to support the soldier, then augmentation technologies should be built around human capabilities, biological tolerances, and humanized to perform in a way that is consistent with human neurobiology. [26][27][28] In the midst of this current robotics and information technology era, there is a greater need than ever before for human performance research that will solve problems to optimize human, human-AI, and human-robot teams.…”

Section: Projecting Forward From the Lessons Learned In Human Performmentioning

confidence: 99%

“…26,27 If the human is the primary weapon system and all technology is to support the soldier, then augmentation technologies should be built around human capabilities, biological tolerances, and humanized to perform in a way that is consistent with human neurobiology. [26][27][28] In the midst of this current robotics and information technology era, there is a greater need than ever before for human performance research that will solve problems to optimize human, human-AI, and human-robot teams. 13 Advances in bioinformatics and the continuous expansion of computing power support the development of ever more sophisticated, integrated and generalizable models.…”

Section: Projecting Forward From the Lessons Learned In Human Performmentioning

confidence: 99%

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The implications of emerging technology on military human performance research priorities

Billing

Fordy²,

Friedl

et al. 2021

Journal of Science and Medicine in Sport

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Objectives: To demonstrate the need for the military human performance research community to anticipate and evolve with the emergence of new and disruptive battlefield technologies that are changing the fundamental role of the human combatant. Methods: An international team of military performance researchers drew on relevant literature and their individual national perspectives and experiences to provide an integrated forecast of research priorities and needs based on current trends. Results: Rapid advances and convergence in fields such as robotics, information technology and artificial intelligence will continue to have a revolutionary impact on the battlefield of the future. The disruption associated with these technologies will most acutely be experienced by the human combatant at the tactical level, with increasing cognitive demands associated with the employment and use of new capabilities. New research priorities may include augmented performance of humans-machine teams, enhanced cognitive and immunological resilience based on exercise neurobiology findings, and psychophysiological stress tolerance developed in realistic but safe synthetic training environments. Solving these challenges will require interdisciplinary research teams that have the capacity to work across the physical, digital and biological boundaries whilst collaborating seamlessly with end-users, human combatants. New research methodologies taking full advantage of sensing technologies will be needed to provide rigorous, evidence-based data in real and near-real world environments. Longer term research goals involving biological manipulation will be shaped by moral, legal and ethical considerations and evolving concepts of what it means to be human. Conclusion: This paper outlines key recommendations to assist military human performance researchers to adapt their practice in order to match the increasing pace of military modernisation. By anticipating technological change and forecasting possible emerging technologies the military human performance research community can manoeuvre to prioritise research activities today in line with future needs and requirements.

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Section: Projecting Forward From the Lessons Learned In Human Performmentioning

confidence: 99%

Section: Projecting Forward From the Lessons Learned In Human Performmentioning

confidence: 99%

The implications of emerging technology on military human performance research priorities

Billing

Fordy²,

Friedl

et al. 2021

Journal of Science and Medicine in Sport

View full text Add to dashboard Cite

show abstract

“…Note that ML techniques can be utilized broadly in other tasks in the group environment, such as deciding optimal group composition and the design of interventions acceptable common ground whatever the displayed initial beliefs of the group's members might be. [40][41][42][43][44][45] An excellent survey of the broad ideas on human-AI autonomy teaming has been put forth in a recent paper. 46 The authors discuss different levels of autonomy and summarize the recent literature.…”

Section: Sociocognitive Constructs For Decision Making In Human-ai Gr...mentioning

confidence: 99%

“…Both humans and AI are subject to bias and faulty information: humans by their members' beliefs and AI by the available data and training protocols. Since observability and predictability have ramifications on the level of trust, 39 groups with AI involvement must have confidence that the behavior of their AI is consistent with an acceptable common ground whatever the displayed initial beliefs of the group's members might be 40–45 …”

Section: Introductionmentioning

confidence: 99%

Predictive models for human–AI nexus in group decision making

Askarisichani

Bullo

Friedkin

et al. 2022

Annals of the New York Academy of Sciences

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Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human-AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human-AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human-AI decision making in a group setting. K E Y W O R D Sdecision making, human-AI teams, machine learning Over the last decades, scientists have made meaningful headway in understanding collective group behavior of humans. Researchers have examined social processes of groups on single issues and sequences of issues, and have understood the implications of these social 70

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“…Specifically for AVs, researchers have worked with physiological signals (i.e., electroencephalography and galvanic skin response) to develop a classifier-based empirical trust sensor [7]. The privileged sensing framework (PSF) was applied with that same type of physiological signals to anticipate and influence humans' behaviors, with the goal of optimizing changes in control authority between the human and the automated system [8], [23]. Classic methods, such as Kalman filtering, have also been used for trust estimation [9].…”

Section: A Related Work: Trust In Automated Systems Trust Estimatiomentioning

confidence: 99%

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

Azevedo-Sa

Jayaraman

Yang

et al. 2020

IEEE Robot. Autom. Lett.

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Automated vehicles (AVs) that intelligently interact with drivers must build a trustworthy relationship with them. A calibrated level of trust is fundamental for the AV and the driver to collaborate as a team. Techniques that allow AVs to perceive drivers' trust from drivers' behaviors and react accordingly are, therefore, needed for context-aware systems designed to avoid trust miscalibrations. This letter proposes a framework for the management of drivers' trust in AVs. The framework is based on the identification of trust miscalibrations (when drivers' undertrust or overtrust the AV) and on the activation of different communication styles to encourage or warn the driver when deemed necessary. Our results show that the management framework is effective, increasing (decreasing) trust of undertrusting (overtrusting) drivers, and reducing the average trust miscalibration time periods by approximately 40%. The framework is applicable for the design of SAE Level 3 automated driving systems and has the potential to improve the performance and safety of driver-AV teams.

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The privileged sensing framework: A principled approach to improved human-autonomy integration

Cited by 14 publications

References 80 publications

The implications of emerging technology on military human performance research priorities

The implications of emerging technology on military human performance research priorities

Predictive models for human–AI nexus in group decision making

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

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