Framing governance for a contested emerging technology:insights from AI policy

Abstract

This paper examines how the governance in AI policy documents have been framed as way to resolve public controversies surrounding AI. It draws on the studies of governance of emerging technologies, the concept of policy framing, and analysis of 49 recent policy documents dedicated to AI which have been prepared in the context of technological hype expecting fast advances of AI that will fundamentally change economy and society. The hype about AI is accompanied by major public controversy about positive and negative effects of AI. Against the backdrop of this policy controversy, governance emerges as one of the frames that diagnoses the problems and offers prescriptions. Accordingly, the current governance characterized by oligopoly of a small number of large companies is indicated as one of the reasons for problems such as lack of consideration of societal needs and concerns. To address these problems, governance frame in AI policy documents assigns more active and collaborative roles to the state and society. Amid public controversies, the state is assigned the roles of promoting and facilitating AI development while at the same time being a guarantor of risk mitigation and enabler of societal engagement. High expectations are assigned to public engagement with multiple publics as a way to increase diversity, representation and equality in AI development and use. While this governance frame might have a normative appeal, it is not specific about addressing some well-known challenges of the proposed governance mode such as risks of capture by vested interests or difficulties to achieve consensus.

Steering the governance of artificial intelligence: national strategies in perspective

“…This study set out to explore how states choose to design AI governance arrangements since 2016. Analyses of recent AI strategies released by for-profits and nonprofits globally (Ulnicane et al, 2020) reveal that various forms of tactical engagement begin to replace ad-hoc responses to the disruptive speed and scope of AI transformations. Many national strategies go in the same direction and discuss how to develop and support scientific research, retain AI talent, and how to enhance skills for future work.…”

“…Since then, a plethora of public and private actors have issued statements on how AI would change society for the better or for the worse, highlighting infrastructural developments, military applications and impact on jobs and human relations. Some of these statements revealed concrete plans to address AI-related challenges, but the majority remained principled positions on limiting risks associated with disruptive technologies (Ulnicane et al, 2020, Jobin et al, 2019. As recognition grows that tools based on algorithmic processing and machine learning bring about as many promises as commotions, governments are under increased pressure to react for the wellbeing of their citizens and for their raison d'être (Taeihagh, 2021).…”

What rules? Framing the governance of artificial agency

“…Using the framework also exposes the 'governors' of AI by identifying those actors or communities that function as authorities in formulating the related rules and therefore, contribute to future studies on the roles and interests of key actors in the governance of AI (Ulnicane et al, 2020;Radu, 2021). Depending on what level of abstraction the framework is applied, this could be said to encompass any actor performing an authoritative function in formulating rules for the design and deployment of AI.…”

“…The creation of inclusive training sets is in this view understood as providing the artificial agent with training in an environment that provides a better reflection of the real world. These solutions form part of a broader general consensus in the AI policy discourse on the importance of inclusivity and diversity in the design of AI systems to ensure that its benefits accrue to all segments of society (Ulnicane et al 2020;Radu, 2021). This mechanism therefore does not specify what the performance measure should be, but defines what the environment should consist of.…”

“…This also overlaps with a broader research agenda on the social impact of algorithmic decision-making that now permeates a range of societal processes; from online search services to high-frequency trading and autonomous vehicles. While the social impact of AI and algorithms has received increasing attention by social science scholars in recent years, less focus has been put on the institutions and processes that govern their development and deployment (Radu, 2021;Ulnicane et al, 2020).…”