Good Systems

Fleischmann, Kenneth R.; Greenberg, Sherri R.; Gurari, Danna; Stangl, Abigale; Verma, Nitin; Day, Jaxsen R.; Simons, Rachel N.; Yeh, Tom

doi:10.1145/3311957.3359437

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…This paper reports findings from a larger interdisciplinary project focusing on designing smart hand tools that can benefit skilled workers such as welders. That project is in turn one of the core research projects for Good Systems, a UT Grand Challenge (Fleischmann, 2020; Fleischmann et al, 2019), an interdisciplinary project at the University of Texas at Austin focusing on defining, building, and evaluating ethical human‐AI partnerships. Researchers from the School of Information, the LBJ School of Public Affairs, the Walker Department of Mechanical Engineering, and the Chandra Family Department of Electrical and Computer Engineering are collaborating to develop solutions that can leverage AI to enhance skilled trades by upskilling workers and improving their work conditions instead of replacing them.…”

Section: Methodsmentioning

confidence: 99%

Welding Instructors' Perspectives on Using AI Technology in Welding Training

Lassiter,

Collier,

Fleischmann

et al. 2023

Proceedings of the Association for Information Science and Tech

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The welding industry in the U.S. faces a serious shortage of skilled welders. The goal of this paper is to explore welding instructors' attitudes toward applications of Augmented Reality (AR) and Virtual Reality (VR) as well as artificial intelligence (AI) in welding training. Such approaches could allow future welders to acquire welding skills faster and in a safer work environment. Also, the welding industry could attract a more diverse group of workers. This paper builds on previous literature and studies researching the use of AR, VR, and AI in welding and in other comparable industries. The paper reports findings from interviews with eight welding instructors at a community college to obtain insights regarding how they believe AR, VR, and AI could be used in welding training. The paper provides implications for using AR, VR, and AI to attract and retain the next generation of welders.

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Section: Methodsmentioning

confidence: 99%

Welding Instructors' Perspectives on Using AI Technology in Welding Training

Lassiter,

Collier,

Fleischmann

et al. 2023

Proceedings of the Association for Information Science and Tech

Self Cite

View full text Add to dashboard Cite

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Collaborative Work with Highly Automated Marine Navigation Systems

Veitch

Dybvik

Steinert

et al. 2022

Comput Supported Coop Work

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In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators’ own accounts in the field. Navigators reflected upon their role as one of ‘backup,’ defined by ad-hoc control takeovers from the automation. Designers positioned navigators ‘in the loop’ of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work.

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