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20232025
most citedBeyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation

21 citations · 27 across the 7 of their papers we have counts for

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cs.CY2024

Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI

Georgios Bakirtzis, Andrea Aler Tubella, Andreas Theodorou +2

Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contempo…

cs.CY20241 cited

Application of the NIST AI Risk Management Framework to Surveillance Technology

Nandhini Swaminathan, David Danks

This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within…

cs.CY2024

Future of Pandemic Prevention and Response CCC Workshop Report

David Danks, Rada Mihalcea, Katie Siek +3

This report summarizes the discussions and conclusions of a 2-day multidisciplinary workshop that brought together researchers and practitioners in healthcare, computer science, an…

cs.CY202421 cited

Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation

Jennifer Chien, David Danks

Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions…

cs.CY20241 cited

Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology

Daniel Trusilo, David Danks

This paper presents a theoretical analysis and practical approach to the moral responsibilities when developing AI systems for non-military applications that may nonetheless be use…