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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http://dx.doi.org/10.1007/s10606-022-09450-7 | DOI Listing |
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Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA.
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View Article and Find Full Text PDFEnviron Res
January 2025
Man-Technology-Environment Research Center (MTM), Örebro University, Örebro SE-701 82, Sweden.
As the volume of plastic waste from electrical and electronic equipment (WEEE) continues to rise, a significant portion is disposed of in the environment, with only a small fraction being recycled. Both disposal and recycling pose unknown health risks that require immediate attention. Existing knowledge of WEEE plastic toxicity is limited and mostly relies on epidemiological data and association studies, with few insights into the underlying toxicity mechanisms.
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Department of Civil Engineering, The University of Mississippi, University, MS 38677, USA.
The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection.
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