20 research outputs found
Exploring Digital Twins as Policy Tools: An Analysis of Emerging Initiatives
No full textCities are among the largest and most complex artefacts created by humans. Yet the advancements in computing capabilities combined with ubiquitous data streams allow for complex socio-technical systems of cities to be abstracted and modelled. This paper discusses the technology of Smart City Digital Twin as a policy tool. Following the ideas of flat ontology, the paper argues that intelligent machines exhibit their own agency, which has to be investigated through the behavioural lens. As making policy decisions based on counterfactual simulations is becoming more widespread, it is crucial not only to simulate how certain policy interventions will affect the life of a city but also to investigate how such models and simulations are designed and behave. Adding a social layer in the form of behavioural data of the population will allow Smart City Digital Twins to be used for a wider spectrum of policy modelling purposes. Such behavioural data can be generated through a task-based approach, where individuals will be asked to conduct certain activities in order to generate synthetic data for situations that require data that does not yet exist. This will not only allow to avoid certain privacy-related concerns but also can be used as a tool for labour provision
Reversing the logic of generative AI alignment: a pragmatic approach for public interest
No full textThe alignment of artificial intelligence (AI) systems with societal values and the public interest is a critical challenge in the field of AI ethics and governance. Traditional approaches, such as Reinforcement Learning with Human Feedback (RLHF) and Constitutional AI, often rely on pre-defined high-level ethical principles. This article critiques these conventional alignment frameworks through the philosophical perspectives of pragmatism and public interest theory, arguing against their rigidity and disconnect with practical impacts. It proposes an alternative alignment strategy that reverses the traditional logic, focusing on empirical evidence and the real-world effects of AI systems. By emphasizing practical outcomes and continuous adaptation, this pragmatic approach aims to ensure that AI technologies are developed according to the principles that are derived from the observable impacts produced by technology applications
Governing AI through interaction: situated actions as an informal mechanism for AI regulation
No full textThis article presents a perspective that the interplay between high-level ethical principles, ethical praxis, plans, situated actions, and procedural norms influences ethical AI practices. This is grounded in six case studies, drawn from fifty interviews with stakeholders involved in AI governance in Russia. Each case study focuses on a different ethical principle—privacy, fairness, transparency, human oversight, social impact, and accuracy. The paper proposes a feedback loop that emerges from human-AI interactions. This loop begins with the operationalization of high-level ethical principles at the company level into ethical praxis, and plans derived from it. However, real-world implementation introduces situated actions—unforeseen events that challenge the original plans. These turn into procedural norms via routinization and feed back into the understanding of operationalized ethical principles. This feedback loop serves as an informal regulatory mechanism, refining ethical praxis based on contextual experiences. The study underscores the importance of bottom-up experiences in shaping AI's ethical boundaries and calls for policies that acknowledge both high-level principles and emerging micro-level norms. This approach can foster responsive AI governance, rooted in both ethical principles and real-world experiences
Situated usage of generative AI in policy education: implications for teaching, learning, and research
No full textThis study explores the contrasting sentiments towards the use of generative AI technologies among research postgraduate students in public policy. 14 interviews about the usage of generative AI technologies in the students’ research, teaching, and learning practices were conducted and used as the empirical data source for this project. Through qualitative and sentiment analysis, the research identified domains where students applied generative AI and discovered both positive and negative sentiments within the same application domains. The divergence in sentiments was interpreted using the ‘plans and situated actions’ framework, suggesting that technological expectations constrained by contextual environments lead to varied experiences of ‘enchantment’ and ‘disenchantment’. The findings emphasize the imperative for adaptable academic policies delineating acceptable AI usage in research, the implementation of discipline-specific AI training in universities, and the development of discipline-specific AI systems to cater to unique academic field needs.</p
The emergence of institutional architecture to govern AI : investigating the state’s role in digital capitalism
No full textNumerous governments issued policy and regulatory documents for AI in the last years to deal with opportunities and challenges posed by this technology. However, a systematic understanding of the roles that the governments are taking is lacking in the academic literature. The analysis of the global landscape of policy initiatives using qualitative content analysis and LDA topic modeling shows that the state has three major roles in governing AI: development, control, and promotion. The analysis of regulatory documents and game theoretic modeling shows that some countries prioritize consumer protection through stringent regulation. In contrast, others promote innovation by adopting a more hands-off approach when balancing a trade-off between regulation and innovation. However, minimal regulation is rationalizable only if a government is not prioritizing consumer welfare but tries to maximize innovation, domestic producer surplus, or perceived consumer welfare. Russia presents an interesting case where the state takes on the role of “development” by actively participating in technological innovation. At the same time, it implements a hands-off approach to regulation through unenforceable ethical principles. Fifty interviews with AI companies, academics, and policymakers in Russia show that companies have little motivation to comply with ethical regulations. The major motivational constraints are profit-seeking behavior for economic motivation, ethical ignorance for normative motivation, lack of credible threat for social motivation, and technological infeasibility for technological motivation. This regulatory regime was formed under the strong influence of big tech companies, which saw an opportunity to avoid regulatory oversight by washing out concrete regulatory measures from the policy. Unenforceable ethics-based self-regulation is a regulatory gift from the Russian government to the industry. By applying the lens of the regulation theory, this form of the state’s intervention in the governance of the socio-technical system is conceptualized as a regulatory regime under the accumulation regime of digital capitalism.</p
The challenges of industry self-regulation of AI in emerging economies: Implications of the case of Russia for public policy and institutional development
No full textThe widespread utilization of AI technologies urges governments worldwide to design governance structures for it. Industry self-regulation is one of the approaches most often suggested for this task, as it allows flexibility in balancing innovation and safety. This book chapter discusses how self-regulatory approaches popular for the governance of AI can potentially be problematic for emerging economies. The findings are derived from the fieldwork conducted in Russia in 2021-2022. The key challenges include the need for more technical expertise within the government, the lack of civil liberties, the interwovenness between the public and the private sector, the lack of motivation for ethical development, and protectionism over the local IT industry. Some initial remedies for the shortcomings of the industry self-regulation for AI in emerging economies can be found in how governments mitigate the negative effects of regulatory capture. These include promoting greater balance and diversity in the competition among different stakeholders, reforming the institutional context within which regulators operate, and opening up the regulatory process to various external checks and balances
The state’s role in governing artificial intelligence: development, control, and promotion through national strategies
No full textAbstractNumerous governments worldwide have issued national artificial intelligence (AI) strategies in the last five years to deal with the opportunities and challenges posed by this technology. However, a systematic understanding of the roles and functions that the governments are taking is lacking in the academic literature. Therefore, this research uses qualitative content analysis and Latent Dirichlet Allocation (LDA) topic modeling methodologies to investigate the texts of 31 strategies from across the globe. The findings of the qualitative content analysis highlight thirteen functions of the state, which include human capital, ethics, R&D, regulation, data, private sector support, public sector applications, diffusion and awareness, digital infrastructure, national security, national challenges, international cooperation, and financial support. We combine these functions into three general themes, representing the state’s role: development, control, and promotion. LDA topic modeling results are also reflective of these themes. Each general theme is present in every national strategy’s text, but the proportion they occupy in the text is different. The combined typology based on two methods reveals that the countries from the post-soviet bloc and East Asia prioritize the theme “development,” highlighting the high level of the state’s involvement in AI innovation. The countries from the EU focus on “control,” which reflects the union’s hard stance on AI regulation, whereas countries like the UK, the US, and Ireland emphasize a more hands-off governance arrangement with the leading role of the private sector by prioritizing “promotion.
The limitation of ethics-based approaches to regulating artificial intelligence: regulatory gifting in the context of Russia
No full textThe effects that artificial intelligence (AI) technologies will have on society in the short- and long-term are inherently uncertain. For this reason, many governments are avoiding strict command and control regulations for this technology and instead rely on softer ethics-based approaches. The Russian approach to regulating AI is characterized by the prevalence of unenforceable ethical principles implemented via industry self-regulation. We analyze the emergence of the regulatory regime for AI in Russia to illustrate the limitations of this approach. The article is based on 50 interviews with policymakers, representatives of AI companies, and academics in the country. The findings show that this regulatory regime was formed under the strong influence of Russian big tech companies, which saw an opportunity to avoid regulatory oversight by washing out concrete regulatory measures from the policy. This approach is part of a broader protectionist sanction-proofing strategy for the local IT sector designed by the government, which can be characterized by lifting regulatory barriers for local companies. Unenforceable ethics-based self-regulation is a regulatory gift from the Russian government to the industry. This gift was intentionally designed because the government thought that prioritizing local innovation over consumer protection would benefit the public. However, the gift can also unintentionally undermine the public interest by providing an opportunity for ethics washing.</p
Governance of Disruptive Emerging Technologies: Regulatory Gifting for Artificial Intelligence in Russia
No full textRegulation of artificial intelligence (AI) is a challenging task because ready-made recipes for good interventions are not available due to the novelty of the evolving issue. Little research investigates the early stages of the development of an institutional architecture for AI governance and the roles that different stakeholders play in this process. This article investigates the formation of a regulatory regime for AI in Russia. The case study shows that information asymmetry between the regulators and the industry and the lack of in-house technological capacities forced the regulator to seek help from the industry while designing the regulation. This allowed the industry to promote a regulatory approach based on self-regulation. The findings have implications within a broader argument that as the information asymmetry about the emerging technologies between the industry and government widens, the influence of industry over the regulation would become stronger
