Facial recognition is the plutonium of AI

By Luke Stark. 2019

117 citations recorded by the source corpus.

It's dangerous, racializing, and has few legitimate uses; facial recognition needs regulation and control on par with nuclear waste.

Subfields: Sociology and Political Science, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “Despite their growing prevalence, their use remains controversial [74].”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “…2019; Buolamwini and Gebru 2018; Lum and Isaac 2016; Noble 2018; Eubanks 2018), exploitative or extractive data practices (Gray and Suri 2019), and applications of AI that dispossess the identity and resources of vulnerable populations (Green 2019; Keyes 2018; Hanna et al. 2019; Stark 2019).”
Addressing Privacy Threats from Machine Learning (2021, background): “Facial recognition algorithms can identify people in public spaces (Garvie et al., 2016; Stark, 2019); targeted advertising algorithms can exploit detailed user profiles to shape behavior at scale (Zuboff, 2018); and predictive policing algorithms can single out individuals for targeted surveillance”
The impact of artificial intelligence on learner–instructor interaction in online learning (2021, background): “Stark (2019) described facial recognition as the plutonium of AI: “[facial recognition] is dangerous, racializing, and has few legitimate uses; facial recognition needs regulation and control on par with nuclear waste.””
Terms of inclusion: Data, discourse, violence (2020, background): “As Greene, Hoffmann, and Stark (2019) have shown in their work on values and ethics statements guiding the development of machine learning and AI, these efforts provide little indication that data technologies can be limited or constrained—they paint ethics as a matter of iterative improvement and…”
Toward situated interventions for algorithmic equity: lessons from the field (2020, background): “Hartzog & Selinger, Stark [30, 60]); the increasing uptake of this policy position in U.”
Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, methodology): “Full or partial bans and moratoria on the deployment of FRTs by local governments and law enforcement agencies have been passed in jurisdictions in the United States, Europe, and elsewhere (Leong, 2019) as the dangers of these technologies to human equality (Stark, 2019) and civil liberties (Hartzog”