Introduction to “Systems Engineering and Artificial Intelligence” and the Chapters

Lawless, William F.; Mittu, Ranjeev; Sofge, Donald A.; Shortell, Thomas; McDermott, Thomas A.

doi:10.1007/978-3-030-77283-3_1

Cited by 4 publications

(10 citation statements)

References 12 publications

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“…Whether unfettered development is encouraged or restrained is a function of interactions among the affected ecosystems. Lethal autonomous weapons systems (LAWS) provide a good example [2]. They will proliferate in a society if other ecosystems that interact with it invest in and legitimize their development, but will be suppressed in any society where the state reins in the military deployment of A-HMT-S.…”

Section: A-hmt-s As An Ecosystemmentioning

confidence: 99%

“…Boeing 737 Max can be regarded as a precursor to full-fledged A-HMT-S. Humans are on the loop rather than in the loop [2]. When they are not given authority to intervene when software made a faulty move, or when they aren't sure how to react to a machine decision, the entire system fails catastrophically.…”

Section: Two Cases Of Failure In Systems That Are "A-hmt-s-adjacent"mentioning

confidence: 99%

“…In this paper, Autonomous Human Machine Teams and Systems (A-HMT-S) are defined as teams that include humans and increasingly intelligent and autonomous machines working together [1,2]. Intelligent machines are defined as machines or algorithms that think by scanning data for patterns, make inferences, and learn by testing inferences [3].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent machines are defined as machines or algorithms that think by scanning data for patterns, make inferences, and learn by testing inferences [3]. Advances in deep learning in the 21st century bring this emerging phenomenon closer to reality [1,2,4], though the idea of thinking machines was explored decades ago by Turing, Shannon, Weiner, Simon, and others, and was foreshadowed to an extent by Babbage's Difference Engines and Analytical Engine a century before that [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although that evolution is far from complete and may never end, quasi-A-HMT-S with semiautonomous machines are now commonplace. They diagnose and treat diseases [9], drive vehicles [1,10], fly airplanes [11,12], educate students, conduct research [4], trade stocks and derivatives [8], market products and services [13], fight wars [2], all with mixed results.…”

Section: Introductionmentioning

confidence: 99%

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Trust in things: A review of social science perspectives on autonomous human-machine-team systems and systemic interdependence

Akiyoshi

2022

Front. Phys.

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For Autonomous Human Machine Teams and Systems (A-HMT-S) to function in a real-world setting, trust has to be established and verified in both human and non-human actors. But the nature of “trust” itself, as established by long-evolving social interaction among humans and as encoded by humans in the emergent behavior of machines, is not self-evident and should not be assumed a priori. The social sciences, broadly defined, can provide guidance in this regard, pointing to the situational, context-driven, and sometimes other-than-rational grounds that give rise to trustability, trustworthiness, and trust. This paper introduces social scientific perspectives that illuminate the nature of trust that A-HMT-S must produce as they take root in society. It does so by integrating key theoretical perspectives: the ecological theory of actors and their tasks, theory on the introduction of social problems into the civic sphere, and the material political economy framework developed in the sociological study of markets.

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Section: A-hmt-s As An Ecosystemmentioning

confidence: 99%

Section: Two Cases Of Failure In Systems That Are "A-hmt-s-adjacent"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trust in things: A review of social science perspectives on autonomous human-machine-team systems and systemic interdependence

Akiyoshi

2022

Front. Phys.

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The self‐regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies

Tang

Koopman

Yam

et al. 2022

Human Resource Management

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Organizations are increasingly augmenting employee jobs with intelligent machines. Although this augmentation has a bright side, in terms of its ability to enhance employee performance, we think there is likely a dark side as well. Draw from self‐regulation theory, we theorize that dependence on intelligent machines is discrepancy‐reducing—enhancing work goal progress, which in turn boosts employees’ task performance. On the other hand, such dependence may be discrepancy‐enlarging—threatening employee self‐esteem, which in turn detracts from employees’ task performance. Drawing further from self‐regulation theory, we submit that employees’ core self‐evaluation (CSE) may influence these effects of dependence on intelligent machines. Across an experience‐sampling field study conducted in India (Study 1) and a simulation‐based experiment conducted in the United States (Study 2), our results generally support a “mixed blessing” perspective of intelligent machines at work. We conclude by discussing the theoretical and practical implications of our work.

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Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

Raz,

Miller,

Chang

et al. 2023

INCOSE International Symp

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Systems Engineering (SE) based test and evaluation (T&E) approaches have proven crucial for successful realization of most modern‐day complex systems and system‐of‐systems solutions. The design, test, and evaluation of engineered systems with many technologically advanced and complex components hinges on well‐structured integration and life cycle evolution process models, clearly defined requirements, along with controllability, observability, and stability (COS) of components that transcend to the system‐level. However, integration of machine learning (ML), deep learning (DL) and artificially intelligent (AI) components invalidates some SE foundations as the DL algorithms are primarily data‐driven with opaque decision‐making constructs. As a result, the current SE T&E approaches, although necessary, have become insufficient to evaluate the adoption of ML/DL/AI methods in engineered systems. This paper proposes two new approaches—explainable AI (xAI) and counterfactual testing and evaluation (cT&E)—as addition to the SE tool set for T&E of intelligent engineered systems. An example of SE T&E considerations is provided based on a conceptual aircraft control system implementation comparing as a classical controller and a DL‐based reinforcement learning controller.

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Introduction to “Systems Engineering and Artificial Intelligence” and the Chapters

Cited by 4 publications

References 12 publications

Trust in things: A review of social science perspectives on autonomous human-machine-team systems and systemic interdependence

Trust in things: A review of social science perspectives on autonomous human-machine-team systems and systemic interdependence

The self‐regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

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