Governing AI – attempting to herd cats? Introduction to the special issue on the Governance of Artificial Intelligence

Büthe, Tim; Djeffal, Christian; Lütge, Christoph; Maasen, Sabine; Ingersleben‐Seip, Nora von

doi:10.1080/13501763.2022.2126515

Cited by 28 publications

(17 citation statements)

References 82 publications

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“…The remaining papers in this special issue focus on various aspects of digital infrastructures and platforms. As with regulatory concerns surrounding the use of AI there is a growing concern across society regarding the use of MLand NLP-based IT artefacts in digital infrastructures and platforms (Blauth, Gstrein, and Zwitter, 2022;Büthe et al, 2022;Chang, 2022;Korngiebel and Mooney, 2021;Mikalef et al, 2022;Wright, 2021). Two books published in 2022, the first titled The Loop by a science and technology journalist for NBC (Ward, 2022), and the second, Stolen Focus by a British investigative journalist (Hari, 2022), present extensive evidence that designers of digital infrastructures deliberately leveraged negative affordances of ML-based AI algorithms to exploit known psychological weaknesses in humans.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, institutional logics inform actors’ choices and the use of the mechanisms that create, bring about change to or maintain, or disrupt institutions and policies (Gozman and Currie, 2013). Take, for example, in discussing AI technologies, Büthe et al (2022, p. 12) state: ‘Laws, regulations and other measures to govern AI thus do not so much reflect inherent characteristics or objective truths about the technology but reflect political actors’ perceptions given those actors’ predisposition – and in the process of framing what needs to be addressed, they construct the technology’.…”

Section: Towards An Ontology For Research On Regulation Of and Throug...mentioning

confidence: 99%

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The regulation of and through information technology: Towards a conceptual ontology for IS research

Butler

Gozman

Lyytinen

2023

Journal of Information Technology

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This special issue addresses a largely neglected area of Information Systems (IS) research – the regulation of and through Information Technology (IT). As with other human technologies, IT artefacts present risks and can harm individuals, groups, organisations, economies, and society: However, this remains a largely unexplored topic in IS research. Nevertheless, regulators, social commentators, the media, and the public have voiced their concerns about such risks, particularly those related to artificial intelligence (AI), cybersecurity, privacy, digital assets (e.g. cryptocurrencies), and the market dominance of digital platforms. Accordingly, regulations have been instituted or proposed to regulate IT artefacts in and across several business sectors. Additionally, in response to the informational challenges posed by a complex web of laws and regulations, regulators and business organisations have implemented IT artefacts to transform regulatory and supervisory processes or to enhance organisational risk management and compliance reporting capabilities. This special issue addresses the research challenges related to emergent issues surrounding the regulation of and through IT. In this editorial, we take stock of where the field currently stands. We advance a conceptual ontology of IT regulation to guide future research by specifying several taken-for-granted core concepts. By rendering the concepts, categories, and their relationships explicit, the model and its related research questions provide a firm foundation to generate a cumulative body of research on the regulation of and through IT.

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Section: Discussionmentioning

confidence: 99%

Section: Towards An Ontology For Research On Regulation Of and Throug...mentioning

confidence: 99%

The regulation of and through information technology: Towards a conceptual ontology for IS research

Butler

Gozman

Lyytinen

2023

Journal of Information Technology

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“…The black box effect of AI algorithms refers to the difficulty in understanding how an AI system arrived at a particular decision or prediction. The algorithm's inner workings and decision-making processes are opaque and difficult to interpret, making it challenging to assess the system's fairness, bias, and accuracy (Büthe et al, 2022(Büthe et al, , p. 1729. These two regulations are centered on fundamental rights and safety first, which is understandable due to the rush to market conditions that companies are precipitated to do because of competition that may lead to less safety, but is only based on costs.…”

Section: Regulatory Comparisonmentioning

confidence: 99%

“…This algorithmic technology is consequently shaped by GDPR regulatory implementation which limits and shapes the data that serves as input to the trainable algorithms. AI can be defined as "systems that combine means of gathering inputs from the external environment with technologies for processing these inputs and relating them algorithmically to previously stored data, allowing such systems to carry out a wide variety of tasks with some degree of autonomy" (Büthe et al, 2022(Büthe et al, , p. 1723 Moreover, is relevant to express that the quality of the outputs is heavily influenced by the quality of the data. The six dimensions of data quality proposed by the International Data Management Association (DAMA) (Ramasamy & Chowdhury, 2020, p. 3) are important considerations for Artificial Intelligence (AI) systems, as they ensure that the data used to train and improve the algorithms is of high quality.…”

Section: Introductionmentioning

confidence: 99%

Governing knowledge and technology: European approach to artificial intelligence

Moreira¹

2023

Preprint

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This study conducts a threefold analysis of the EU proposal for an Artificial Intelligence Act (AIA). The first objective is a regulatory analysis of the proposal, focusing on the proposed structures for implementation, concepts, and key requirements for Artificial Intelligence (AI) producers. The second objective is a comparison with the General Data Protection Regulation (GDPR), and its complementarity in providing a robust response to the needs of operators and users of data-driven algorithmic technologies. The third objective is to examine the potential for harmonization of the EU internal market and competitiveness with non-EU markets. The analysis includes a regulatory comparison with the GDPR, which highlights the EU's digital economy policy based on national authorities, risk-based approaches and European bodies for harmonization. The analytical framework of the Brussels Effect is also applied to the AIA proposal, which expresses the intentions of the regulations, both from internal and external pressures. The study concludes that the AIA proposed by the European Commission has the potential to have a significant impact on the development and use of AI systems, particularly in the EU and for companies operating internationally. However, it also poses challenges in terms of implementation and enforcement, which could hamper growth.

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“…AI, which encompasses both rule‐based and learning‐based systems (Büthe et al, 2022), has pervasive applications across industries, in social relationships, and in science. It could potentially add as much as 13 trillion US dollar to global economic output by 2030, increasing global GDP by approximately 1.2% per year (Bughin et al, 2018), if its potential is fully exploited and its risks adequately mitigated.…”

Section: Introductionmentioning

confidence: 99%

Competition and cooperation in artificial intelligence standard setting: Explaining emergent patterns

Ingersleben‐Seip

2023

Review of Policy Research

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Efforts to set standards for artificial intelligence (AI) reveal striking patterns: technical experts hailing from geopolitical rivals, such as the United States and China, readily collaborate on technical AI standards within transnational standard‐setting organizations, whereas governments are much less willing to collaborate on global ethical AI standards within international organizations. Whether competition or cooperation prevails can be explained by three variables: the actors that make up the membership of the standard‐setting organization, the issues on which the organization's standard‐setting efforts focus, and the “games” actors play when trying to set standards within a particular type of organization. A preliminary empirical analysis provides support for the contention that actors, issues, and games affect the prospects for cooperation on global AI standards. It matters because shared standards are vital for achieving truly global frameworks for the governance of AI. Such global frameworks, in turn, lower transaction costs and the probability that the world will witness the emergence of AI systems that threaten human rights and fundamental freedoms.

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Governing AI – attempting to herd cats? Introduction to the special issue on the Governance of Artificial Intelligence

Cited by 28 publications

References 82 publications

The regulation of and through information technology: Towards a conceptual ontology for IS research

The regulation of and through information technology: Towards a conceptual ontology for IS research

Governing knowledge and technology: European approach to artificial intelligence

Competition and cooperation in artificial intelligence standard setting: Explaining emergent patterns

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