Bridging the gap: the case for an ‘Incompletely Theorized Agreement’ on AI policy

Stix, Charlotte; Maas, Matthijs M.

doi:10.1007/s43681-020-00037-w

Cited by 17 publications

(11 citation statements)

References 72 publications

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“…Other recent work has covered the topic from various angles, such as the role of international standards [1], national AI strategies [26] or ethics guidelines [21,27]. Valuable contributions have also been made theorizing about the governance design of international agreements [16,28].…”

Section: Literature Reviewmentioning

confidence: 99%

Mapping global AI governance: a nascent regime in a fragmented landscape

Schmitt

2021

AI Ethics

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The rapid advances in the development and rollout of artificial intelligence (AI) technologies over the past years have triggered a frenzy of regulatory initiatives at various levels of government and the private sector. This article describes and evaluates the emerging global AI governance architecture and traces the contours of a nascent regime in a fragmented landscape. To do so, it organizes actors and initiatives in a two-by-two matrix, distinguishing between the nature of the driving actor(s) and whether or not their actions take place within the existing governance architecture. Based on this, it provides an overview of key actors and initiatives, highlighting their trajectories and connections. The analysis shows international organizations’ high levels of agency in addressing AI policy and a tendency to address new challenges within existing frameworks. Lastly, it is argued that we are witnessing the first signs of consolidation in this fragmented landscape. The nascent AI regime that emerges is polycentric and fragmented but gravitates around the Organisation for Economic Co-Operation and Development (OECD), which holds considerable epistemic authority and norm-setting power.

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Section: Literature Reviewmentioning

confidence: 99%

Mapping global AI governance: a nascent regime in a fragmented landscape

Schmitt

2021

AI Ethics

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“…If implemented, governments could then focus on regulating the regulators instead of the entire AI industry, and AI regulation would benefit from the speed of private innovation. Agile regulations might seem objectionable from ethical perspectives that assert regulations should perfectly reflect normative moral principles, but society will make faster progress towards Ethical AI if we have imperfect AI public policies in place than if we have no Ethical AI policy incentives or regulations at all (Stix and Maas, 2021). Successful Ethical AI policy will not only make it more likely that AI product teams create AIs that align with our collective moral principles, it will help society trust the AIs that can truly make our lives better.…”

Section: Investment Area 4: Agile Public Policy and Regulationmentioning

confidence: 99%

Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

Borg

2021

Big Data & Society

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Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

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“…However, even experts struggle to agree [ 24 , 38 ] and given the ubiquitous nature of AI it is difficult, if not impossible, to forecast now what AI governance frameworks might be needed in the near or far future. In addition, existing institutions, their policy texts and legal frameworks are historically often drawn from in moments of unexpected change [ 50 ], because it can be quicker to react with what exists than develop something new.…”

Section: Motivation Urgency and Limitationsmentioning

confidence: 99%

“…Different institutional set-ups will yield different path dependencies. Those that do get put into motion over the coming years are likely to be especially critical (Stix and Maas 2021) because they form the lens through which governments will be able to interact with progressing AI technologies and enact appropriate AI governance measures. This wouldn't merit outsized concern, if it were possible to clearly forecast AI progress across various sectors and if there was common expert consensus about the development path of AI over the coming decades.…”

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confidence: 99%

Foundations for the future: institution building for the purpose of artificial intelligence governance

Stix

2021

AI Ethics

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Governance efforts for artificial intelligence (AI) are taking on increasingly more concrete forms, drawing on a variety of approaches and instruments from hard regulation to standardisation efforts, aimed at mitigating challenges from high-risk AI systems. To implement these and other efforts, new institutions will need to be established on a national and international level. This paper sketches a blueprint of such institutions, and conducts in-depth investigations of three key components of any future AI governance institutions, exploring benefits and associated drawbacks: (1) “purpose”, relating to the institution’s overall goals and scope of work or mandate; (2) “geography”, relating to questions of participation and the reach of jurisdiction; and (3) “capacity”, the infrastructural and human make-up of the institution. Subsequently, the paper highlights noteworthy aspects of various institutional roles specifically around questions of institutional purpose, and frames what these could look like in practice, by placing these debates in a European context and proposing different iterations of a European AI Agency. Finally, conclusions and future research directions are proposed.

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Bridging the gap: the case for an ‘Incompletely Theorized Agreement’ on AI policy

Cited by 17 publications

References 72 publications

Mapping global AI governance: a nascent regime in a fragmented landscape

Mapping global AI governance: a nascent regime in a fragmented landscape

Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

Foundations for the future: institution building for the purpose of artificial intelligence governance

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