Bring voices from the coast into the Fukushima treated water debate

Mabon, Leslie; Kawabe, Mitsuo

doi:10.1073/pnas.2205431119

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Reducing the diversity of opinion, engineering and physical science expertise was over-represented among government expert committees providing advice on managing the water to be treated and released [41]. Conspicuously absent from the decision-making processes were local fisheries cooperatives, coastal residents, social science and humanities researchers, and neighboring countries and regions including China, Hong Kong, Taiwan, and South Korea.…”

Section: What Is Knowledge To Social Scientists?mentioning

confidence: 99%

“…Conspicuously absent from the decision-making processes were local fisheries cooperatives, coastal residents, social science and humanities researchers, and neighboring countries and regions including China, Hong Kong, Taiwan, and South Korea. A more inclusive approach has been recommended by the scientific community with opinion pieces in prominent news outlets [39]; [41]. A New York Times article is also highly critical of the limited transparency of the deliberations [42].…”

Section: What Is Knowledge To Social Scientists?mentioning

confidence: 99%

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Shannon Holes, Black Holes and Knowledge: Can a Machine become a “Self-Aware" Teammate?

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Moskowitz

2024

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We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

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Section: What Is Knowledge To Social Scientists?mentioning

confidence: 99%

Section: What Is Knowledge To Social Scientists?mentioning

confidence: 99%