AI Governance Research Articles

The Law and Political Economy of AI Governance in India

The Law and Political Economy of AI Governance in India

Procurement as an AI Governance Change Agent

Procurement as an AI Governance Change Agent

Public engagement and AI: A values analysis of national strategies

Calls for public engagement and participation in AI governance align strongly with a public value management approach to public administration. Simultaneously, the prominence of commercial vendors and consultants in AI discourse emphasizes market value and efficiency in a way often associated with the private sector and New Public Management. To understand how this might influence the consolidation of AI governance regimes and decision-making by public administrators, 16 national strategies for AI are subjected to content analysis. References to the public's role and public engagement mechanisms are mapped across national strategies, as is the articulation of values related to professionalism, efficiency, service, engagement, and the private sector. Though engagement rhetoric is common, references to specific engagement mechanisms and activities are rare. Analysis of value relationships highlights congruence of engagement values with professionalism and private sector values, and raises concerns about neoliberal technology frames that normalize AI, obscuring policy complexity and trade-offs.

Trustworthy AI and Corporate Governance: The EU\u2019s Ethics Guidelines for Trustworthy Artificial Intelligence from a Company Law Perspective

AI will change many aspects of the world we live in, including the way corporations are governed. Many efficiencies and improvements are likely, but there are also potential dangers, including the threat of harmful impacts on third parties, discriminatory practices, data and privacy breaches, fraudulent practices and even ‘rogue AI’. To address these dangers, the EU published ‘The Expert Group’s Policy and Investment Recommendations for Trustworthy AI’ (the Guidelines). The Guidelines produce seven principles from its four foundational pillars of respect for human autonomy, prevention of harm, fairness, and explicability. If implemented by business, the impact on corporate governance will be substantial. Fundamental questions at the intersection of ethics and law are considered, but because the Guidelines only address the former without (much) reference to the latter, their practical application is challenging for business. Further, while they promote many positive corporate governance principles—including a stakeholder-oriented (‘human-centric’) corporate purpose and diversity, non-discrimination, and fairness—it is clear that their general nature leaves many questions and concerns unanswered. In this paper we examine the potential significance and impact of the Guidelines on selected corporate law and governance issues. We conclude that more specificity is needed in relation to how the principles therein will harmonise with company law rules and governance principles. However, despite their imperfections, until harder legislative instruments emerge, the Guidelines provide a useful starting point for directing businesses towards establishing trustworthy AI.

The impossible necessity of AI governance

The impossible necessity of AI governance

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

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

AI Governance Oriented Legal to Technology Bridging Framework for Cross-modal Privacy Protection

AI Governance Oriented Legal to Technology Bridging Framework for Cross-modal Privacy Protection

Ethics Guidelines for Artificial Intelligence: Comparing the European and Chinese Approaches

As Europe is a weaker actor mainly due to her digital underdevelopment, the EU is settling on the regulatory side of digital sovereignty. The article is to comparatively analyze the European and Chinese AI ethical guidelines considering the strategic and normative scope of the guidelines as well as their implications on the legal frameworks of AI both in Europe and China. In this field, the most important initiative in the EU was carried on by the High-Level Expert Group on Artificial Intelligence, which, in 2019, released the “Ethics Guidelines for Trustworthy AI,” a catalogue of principles as well as operative measures to achieve Trustworthy AI. In China, instead, the most important initiative was the “Beijing AI Principles” released in 2019 by the Beijing Academy of Artificial Intelligence, and the “Principles to Develop Responsible AI for the New Generation Artificial Intelligence: Developing Responsible Artificial Intelligence” released in 2019 by the New Generation AI Governance Expert Committee.

Researching AI and Data Governance: Meta-Reflections on Research Methods and Practice

Methodology is simply an organised way of doing research. This paper offers a collection of meta-reflections on how the research team at the Centre for AI and Data Governance (CAIDG) conducts high-quality research, with the aim to influence the AI and Data Governance scholarly discourses and shape the policy-making agenda on a local, regional and global level. Within the context of critical contemporary challenges posed by AI and big data, the value of interdisciplinary research collaboration as offering original research analysis with clear policy implications and outputs becomes apparent. CAIDG’s research approach strikes a balance between producing academically rigorous research built on clear theoretical foundations, aligned with creative method applications to meet the demands of emerging and nascent fields of academic scholarship (i.e., AI governance, law and technology, STS studies). This paper is built on a series of interviews – full transcripts are included – with the research population to capture their methodological experiences of integrating the wider interests of society into their academic research at CAIDG.

Good AI for the Present of Humanity Democratizing AI Governance

What do Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek to explore future social and ethical problems that our technological advances may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? We would like to advocate in this article for a more Cyberpunk way of doing AI Ethics, with a more democratic way of governance. In this study, we will seek to expose some of the deficits of the underlying power structures of the AI industry, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all.’

Forecasting AI progress: A research agenda

Forecasting AI progress is essential to reducing uncertainty in order to appropriately plan for research efforts on AI safety and AI governance. While this is generally considered to be an important topic, little work has been conducted on it and there is no published document that gives a balanced overview of the field. Moreover, the field is very diverse and there is no published consensus regarding its direction. This paper describes the development of a research agenda for forecasting AI progress which utilized the Delphi technique to elicit and aggregate experts’ opinions on what questions and methods to prioritize. Experts indicated that a wide variety of methods should be considered for forecasting AI progress. Moreover, experts identified salient questions that were both general and completely unique to the problem of forecasting AI progress. Some of the highest priority topics include the validation of (partially unresolved) forecasts, how to make forecasts action-guiding, and the quality of different performance metrics. While statistical methods seem more promising, there is also recognition that supplementing judgmental techniques can be quite beneficial.

Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs)

AI governance is like one of those mythical creatures that everyone speaks of but which no one has seen. Sometimes, it is reduced to a list of shared principles such as transparency, non-discrimination, and sustainability; at other times, it is conflated with specific mechanisms for certification of algorithmic solutions or ways to protect the privacy of personal data. We suggest a conceptual and normative approach to AI governance in the context of a global digital public goods ecosystem to enable progress on the UN Sustainable Development Goals (SDGs). Conceptually, we propose rooting this approach in the human capability concept—what people are able to do and to be, and in a layered governance framework connecting the local to the global. Normatively, we suggest the following six irreducibles: a. human rights first; b. multi-stakeholder smart regulation; c. privacy and protection of personal data; d. a holistic approach to data use captured by the 3Ms—misuse of data, missed use of data and missing data; e. global collaboration (‘digital cooperation’); f. basing governance more in practice, in particular, thinking separately and together about data and algorithms. Throughout the article, we use examples from the health domain particularly in the current context of the Covid-19 pandemic. We conclude by arguing that taking a distributed but coordinated global digital commons approach to the governance of AI is the best guarantee of citizen-centered and societally beneficial use of digital technologies for the SDGs.

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI

Combining AI with Corporate Governance to Enhance Operational Efficiency of Universities

Since the 21st century, more and more attention has been paid to the education of college students in our country, and the curriculum reform has been actively carried out. It is necessary to cultivate students’ core quality and improve their ability to adapt to society and self-learning. Under the new situation, universities should constantly reform, update their teaching concepts, adopt the latest teaching equipment and ideas, readjust their teaching methods and plans, and provide reference for the future. According to relevant research, the administrative system of universities affects the cultivation of students’ quality and ability to a certain extent. This paper combines AI and corporate governance to explore the influence of the administrative mechanism on the operation of universities under the new situation, and puts forward some solutions and suggestions.

Artificial Intelligence, Forward‐Looking Governance and the Future of Security

AbstractOver the last years, AI applications have come to play a role in many security‐related fields. In this paper, we show that scholars who want to study AI’s link to power and security should widen their perspective to include conceptual approaches from science and technology studies (STS). This way, scholars can pay attention to critical dynamics, processes, practices, and non‐traditional actors in AI politics and governance. We introduce two STS‐inspired concepts – the micro‐politics of design and development and co‐production – and show how the study of AI and security could benefit from them. In the final section, we turn to the study of AI in the context of Switzerland to underscore what aspects the two previously introduced concepts help to highlight that remain invisible for traditional approaches.

AI auditing and impact assessment: according to the UK information commissioner\u2019s office

As the use of data and artificial intelligence systems becomes crucial to core services and business, it increasingly demands a multi-stakeholder and complex governance approach. The Information Commissioner's Office’s ‘Guidance on the AI auditing framework: Draft guidance for consultation’ is a move forward in AI governance. The aim of this initiative is toward producing guidance that encompasses both technical (e.g. system impact assessments) and non-engineering (e.g. human oversight) components to governance and represents a significant milestone in the movement towards standardising AI governance. This paper will summarise and critically evaluate the ICO effort and try to anticipate future debates and present some general recommendations.

Contemporary Approaches for AI Governance in Financial Institutions

Organizations want to leverage AI systems by effective use of their data and reduction of risk espoused throughout the operation of AI systems. Given the aim for risk reduction, we see the need for highlighting several AI governance areas. In the paper we explore risks and limits connected with the application of AI models in financial modeling and its application in practice. We will present approaches to overcome limitations and potential biases. Based on an example model, we highlight certain issues and problems. We review current policies and present their requirements. Finally, we discuss how many policies are aligned with the technical possibilities and limits and based on that develop suggestions for changes.

From Private Regulation to Power Politics: The Rise of China in AI Private Governance Through Standardisation

This article explores different accounts of private regulation in Artificial Intelligence (PRAI) and asks whose views are being implemented in the development of non-state rules for AI. This question is explored through a normative analysis of the political economy of technology and ethical standardisation. The research characterises the distributional outcomes of private regulation, showing how private regulation is currently shaping AI governance. The article argues that the current AI governance framework is built not only on its technical and ethical layers but also -and perhaps most importantly- on the institutional and procedural architecture of international standardisation. Drawing on empirical research, the article finds an increasing role of China in private governance and suggests that the incorporation of ethical discussions into standard-setting would be a first building block in the formation of forthcoming AI governance in an imminently hyperconnected world.

Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance

Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance

Should We Trust a Black Box to Safeguard Human Rights? a Comparative Analysis of Ai Governance

The race to take advantage of the numerous economic, security, and social opportunities made possible by artificial intelligence (AI) is on with nations, intergovernmental organizations, cities, and firms publishing an array of AI strategies. Simultaneously, there are various efforts to identify and distill an array of AI norms. Thus far, there has been limited effort to mine existing AI strategies to see whether common AI norms such as transparency, human-centered design, accountability, awareness, and public benefit are entering into these strategies. Such data is vital to identify areas of convergence and divergence that could highlight opportunities for further norm development in this space by crystallizing State practice. This Article analyzes more than forty existing national AI strategies paying particular attention to the U.S. context, and then comparing those strategies with private-sector efforts and addressing common criticisms of this process within a polycentric framework. Our findings support the contention that State practices are converging around certain AI principles, focusing primarily upon public benefit. AI is a critical component of international peace, security, and sustainable development in the twenty-first century, and as such reaching consensus on AI governance will become vital to help build bridges and trust.