The ghost of AI governance past, present and future: AI governance in the European Union

Stix, Charlotte

doi:10.2139/ssrn.3882493

Cited by 5 publications

(3 citation statements)

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“…Binding legislation is a key mechanism for implementing ethics principles. In this domain, the proposed European Union (EU) AI Act (European Commission 2021) envisions a system of actors, such as national supervisory authorities and notified bodies, involved in certifying the compliance of high-risk AI systems with the new regulation (Stix 2022).…”

Section: Ai Auditing and Responsible Ai Principlesmentioning

confidence: 99%

What about investors? ESG analyses as tools for ethics-based AI auditing

2022

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Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only about legal compliance; organizations can strive to go beyond legal requirements by proactively considering the risks inherent in their AI systems. In the past decade, investors have become increasingly active in advancing corporate social responsibility and sustainability practices. Including nonfinancial information related to environmental, social, and governance (ESG) issues in investment analyses has become mainstream practice among investors. However, the AI auditing literature is mostly silent on the role of investors. The current study addresses two research questions: (1) how companies’ responsible use of AI is included in ESG investment analyses and (2) what connections can be found between principles of responsible AI and ESG ranking criteria. We conducted a series of expert interviews and analyzed the data using thematic analysis. Awareness of AI issues, measuring AI impacts, and governing AI processes emerged as the three main themes in the analysis. The findings indicate that AI is still a relatively unknown topic for investors, and taking the responsible use of AI into account in ESG analyses is not an established practice. However, AI is recognized as a potentially material issue for various industries and companies, indicating that its incorporation into ESG evaluations may be justified. There is a need for standardized metrics for AI responsibility, while critical bottlenecks and asymmetrical knowledge relations must be tackled.

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Section: Ai Auditing and Responsible Ai Principlesmentioning

confidence: 99%

What about investors? ESG analyses as tools for ethics-based AI auditing

2022

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“…This mediating role as the first port of call will become more apparent if recognizable local centers emerge as 'faces' for the EU RAI ecosystem (cf. Stix, 2022).…”

Section: Rootingmentioning

confidence: 99%

Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame

2022

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Governing artificial intelligence (AI) requires cooperation, although the collaboration’s form remains unclear. Technological frames provide a theoretical perspective for understanding how actors interpret a technology and act upon its development, use, and governance. However, we know little about how actors shape technological frames. In this paper, we study the shaping of the technological frame of the European ecosystem for responsible AI (RAI). Through an analysis of EU documents, we identified four expectations that constitute the EU’s technological frame for the RAI ecosystem. Moreover, through interviews with RAI actors, we revealed five types of expectation work responding to this frame: reproducing, translating, and extending (congruent expectation work), and scrutinizing and rooting (incongruent expectation work). Furthermore, we conceptualize expectation work as actors’ purposive actions in creating and negotiating expectations. Our study contributes to the literature on technological frames, technology-centered ecosystems, and RAI while also elucidating the dimensions and co-shaping of technological frames.

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“…Although a majority of the work was conducted internally, the AI HLEG shared their progress in meetings open to institutional observers. They solicited feedback on their first draft of the ethics guidelines half a year into the process via the AI Alliance [80,81], a platform through which the public and institutional actors were able to interact with the AI HLEG. In that first draft [1], the conceptualization proposed by the AI HLEG was that AI should: (1) "respect fundamental rights, applicable regulation and core principles and values, ensuring an 'ethical purpose'" and (2) "be technically robust and reliable since, even with good intentions, a lack of technological mastery can cause unintentional harm. "…”

Section: "Trustworthy Ai:" the Origin Storymentioning

confidence: 99%

Artificial intelligence by any other name: a brief history of the conceptualization of “trustworthy artificial intelligence”

Stix

2022

Discov Artif Intell

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Recent years have seen an increase in artificial intelligence (AI) capabilities and incidents. Correspondingly, there has been an influx of government strategies, panels, dialogues and policy papers, including efforts to regulate and standardize AI systems [12,20,37,52]. A first step in most of these efforts is to delineate the scope of the resulting document, typically by either outlining a range of standard technical definitions of AI [76,85] or referencing existing scholarly work [73]. After defining their scope, many policy documents published by governments delve deeper into the 'type' of AI they wish to solicit from industry players and deploy nationally or globally. This largely serves to ensure that the strategies, policy discussions and AI-related milestones sketched within these documents are guided by a 'north star' , or overarching goal. The north star should be comprehensible to all who read and implement the document. Describing the north star allows a non-technical audience to follow and partake in the relevant policy discussions, though it does not replace technical definitions. Although more could be said as to why this is being done and whether it is sensible, such discussion is outside the scope of this paper. Instead, I focus on and contextualize some of these 'north star' definitions themselves. In particular, I explore one of the most prominent recent descriptions: the EU's concept of "trustworthy AI. " I explain its background, its international effects and its drawbacks in more depth. What is in a name? What is in "trustworthy AI?".

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The ghost of AI governance past, present and future: AI governance in the European Union

Cited by 5 publications

References 0 publications

What about investors? ESG analyses as tools for ethics-based AI auditing

What about investors? ESG analyses as tools for ethics-based AI auditing

Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame

Artificial intelligence by any other name: a brief history of the conceptualization of “trustworthy artificial intelligence”

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