The global governance on automated facial recognition (AFR): ethical and legal opportunities and privacy challenges

Bu, Qingxiu

doi:10.1365/s43439-021-00022-x

Cited by 20 publications

(8 citation statements)

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“…Overall, ethical principles and guidelines drive AIG to address human rights concerns, such as bias, discrimination and privacy (de Almeida et al. , 2021; Bu, 2021). Ethical values can be characterized as the foundation of AIG and the desired output because they represent the human values that should be embedded in governed AI technologies.…”

Section: Conceptualizations Of Ai Governance and Themes In The Ai Gov...mentioning

confidence: 99%

AI governance: themes, knowledge gaps and future agendas

Birkstedt

Minkkinen

Tandon

et al. 2023

INTR

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PurposeFollowing the surge of documents laying out organizations' ethical principles for their use of artificial intelligence (AI), there is a growing demand for translating ethical principles to practice through AI governance (AIG). AIG has emerged as a rapidly growing, yet fragmented, research area. This paper synthesizes the organizational AIG literature by outlining research themes and knowledge gaps as well as putting forward future agendas.Design/methodology/approachThe authors undertake a systematic literature review on AIG, addressing the current state of its conceptualization and suggesting future directions for AIG scholarship and practice. The review protocol was developed following recommended guidelines for systematic reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).FindingsThe results of the authors’ review confirmed the assumption that AIG is an emerging research topic with few explicit definitions. Moreover, the authors’ review identified four themes in the AIG literature: technology, stakeholders and context, regulation and processes. The central knowledge gaps revealed were the limited understanding of AIG implementation, lack of attention to the AIG context, uncertain effectiveness of ethical principles and regulation, and insufficient operationalization of AIG processes. To address these gaps, the authors present four future AIG agendas: technical, stakeholder and contextual, regulatory, and process. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.Research limitations/implicationsTo address the identified knowledge gaps, the authors present the following working definition of AIG: AI governance is a system of rules, practices and processes employed to ensure an organization's use of AI technologies aligns with its strategies, objectives, and values, complete with legal requirements, ethical principles and the requirements set by stakeholders. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.Practical implicationsFor practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.Social implicationsFor society, the authors review elucidates the multitude of stakeholders involved in AI governance activities and complexities related to balancing the needs of different stakeholders.Originality/valueBy delineating the AIG concept and the associated research themes, knowledge gaps and future agendas, the authors review builds a foundation for organizational AIG research, calling for broad contextual investigations and a deep understanding of AIG mechanisms. For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

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Section: Conceptualizations Of Ai Governance and Themes In The Ai Gov...mentioning

confidence: 99%

AI governance: themes, knowledge gaps and future agendas

Birkstedt

Minkkinen

Tandon

et al. 2023

INTR

View full text Add to dashboard Cite

show abstract

“…However, over the past few years, various risks emerged that indicate limitations to the beneficial impact of A.I. These are primarily data bias and algorithmic discrimination (Strauß 2021), surveillance and privacy invasion (Bu 2021), information disorder/disinformation (Ahmed 2021), and cyber-war/cyber-crime (Owe and Baum 2021;Ring 2021). A.I.…”

Section: News Coverage Of Ai Risk Discourses and Critical Data Literacymentioning

confidence: 99%

The news framing of artificial intelligence: a critical exploration of how media discourses make sense of automation

Nguyen

Hekman

2022

AI & Soc

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Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The Guardian, Wired, and Gizmodo. The main goals are to identify dominant emphasis frames in AI news reporting over the past decade, to explore whether certain A.I. frames are associated with specific data risks (surveillance, data bias, cyber-war/cyber-crime, and information disorder), and what journalists and experts contribute to the media discourse. An automated content analysis serves for inductive frame detection (N = 3098), identification of risk references (dictionary-based), and network analysis of news writers. The results show how A.I.’s ubiquity emerged rapidly in the mid-2010s, and that the news discourse became more critical over time. It is further argued that A.I. news reporting is an important factor in building critical data literacy among lay audiences.

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“…Doing so actually goes against the conceptual principle of informed consent: The ethics board or regulation pushes for a short-sighted decision that reduces friction but whose consequences, in the long term, are negative to the community (and in some cases, the participants), not to mention to society at large, via the funding of additional bouts of the same data collection which would have been unnecessary had the original data been securely shared. Ideally, regulations and laws for the protection of individuals and groups should take into account positive and negative impacts, both in the short and longer term (Bu, 2021).…”

Section: Thinking Beyond Box-tickingmentioning

confidence: 99%