Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry (...) for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future. (shrink)

Cultural differences pose a serious challenge to the ethics and governance of artificial intelligence from a global perspective. Cultural differences may enable malignant actors to disregard the demand of important ethical values or even to justify the violation of them through deference to the local culture, either by affirming the local culture lacks specific ethical values, e.g., privacy, or by asserting the local culture upholds conflicting values, e.g., state intervention is good. One response to this challenge is the human (...) rights approach to AI governance, which is intended to be a universal and globally enforceable framework. The proponents of the approach, however, have so far neglected the challenge from cultural differences or left out the implications of cultural diversity in their works. This is surprising because human rights theorists have long recognized the significance of cultural pluralism for human rights. Accordingly, the approach may not be straightforwardly applicable in “non-Western” contexts because of cultural differences, and it may also be critiqued as philosophically incomplete insofar as the approach does not account for the role of culture. This commentary examines the human rights approach to AI governance with an emphasis on cultural values and the role of culture. Particularly, I show that the consideration of cultural values is essential to the human rights approach for both philosophical and instrumental reasons. (shrink)

It is increasingly recognized that as artificial intelligence becomes more powerful and pervasive in society and creates risks and ethical issues that cross borders, a global approach is needed for the governance of these risks. But why, exactly, do we need this and what does that mean? In this Open Forum paper, author argues for global governance of AI for moral reasons but also outlines the governance challenges that this project raises.

A recent study in Australasia (Selvaratnam & Venaruzzo, 2023) revealed some challenges and gaps in the governance of AI and data in higher education, mainly from the human-centeredness perspectives of accessibility, inclusivity and wellbeing. This paper is a narrative review to discern principles of a human-centered approach to the governance of artificial intelligence (AI), benchmarking literature, policies and practice across diverse geopolitical contexts for higher education, synthesizing the review results to provide guiding principles that can support this.

In this commentary, we respond to the editorial letter by Professor Luciano Floridi entitled “AI as a public service: Learning from Amsterdam and Helsinki.” Here, Floridi considers the positive impact of municipal AI registers, which collect a limited number of algorithmic systems used by the city of Amsterdam and Helsinki. We question a number of assumptions about AI registers as a governance model for automated systems. We start with recent attempts to normalize AI by decontextualizing and depoliticizing it, which (...) is a fraught political project that encourages what we call ‘ethics theater’ given the proven dangers of using these systems in the context of the digital welfare state. We agree with Floridi that much can be learned from these registers about the role of AI systems in municipal city management. The lessons we draw, on the basis of our extensive ethnographic engagement with digital wellfare states, are distinctly less optimistic. (shrink)

Artificial Intelligence (AI) has the potential to influence people’s lives in various ways as it is increasingly integrated into important decision-making processes in key areas of society. While AI offers opportunities, it is also associated with risks. These risks have sparked debates about how AI should be regulated, whether through government regulation or industry self-regulation. AI-related risk perceptions can be shaped by national cultures, especially the cultural dimension of uncertainty avoidance. This raises the question of whether people in countries with (...) higher levels of uncertainty avoidance might have different preferences regarding AI regulation than those with lower levels of uncertainty avoidance. Therefore, using Hofstede’s uncertainty avoidance scale and data from ten European countries (N = 7.855), this study investigates the relationships between uncertainty avoidance, people’s AI risk perceptions, and their regulatory preferences. The findings show that people in countries with higher levels of uncertainty avoidance are more likely to perceive AI risks in terms of a lack of accountability and responsibility. While people’s perceived AI risk of a lack of accountability exclusively drives their preferences for government regulation of AI, the perceived AI risk of a lack of responsibility can foster people’s requests for government regulation and/or industry self-regulation. This study contributes to a better understanding of which mechanisms shape people’s preferences for AI regulation. (shrink)

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They (...) argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master’s in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. (shrink)

This article provides a comparative analysis of the regulatory landscapes governing artificial intelligence (AI) in healthcare in the European Union (EU) and South Africa (SA). It critically examines the approaches, frameworks and mechanisms each jurisdiction employs to balance innovation with ethical considerations, patient safety, data privacy and accountability. The EU’s proactive stance, embodied by the AI Act, offers a structured and risk-based categorisation for AI applications, emphasising stringent guidelines for risk management, data governance and human oversight. In contrast, SA’s (...) regulatory environment is characterised by its infancy and lack of specificity, with existing legislation such as the National Health Act and the Medicines and Related Substances Act providing a foundational but limited framework for addressing the unique challenges posed by AI in healthcare. The article delves into the dynamic nature of AI technologies, highlighting the need for continuous risk assessment, the importance of transparent and responsible data governance and the critical role of human oversight in ensuring patient safety and autonomy. It discusses the obligation of clear liability frameworks to address potential malfunctions and security breaches in AI applications. Through this comparative lens, the manuscript identifies regulatory gaps and proposes that the South African Law Reform Commission (SALRC) should play a predominant role in developing draft legislation for AI prior to the evolution of challenges related to these technologies. (shrink)

Human oversight has become a key mechanism for the governance of artificial intelligence (“AI”). Human overseers are supposed to increase the accuracy and safety of AI systems, uphold human values, and build trust in the technology. Empirical research suggests, however, that humans are not reliable in fulfilling their oversight tasks. They may be lacking in competence or be harmfully incentivised. This creates a challenge for human oversight to be effective. In addressing this challenge, this article aims to make three (...) contributions. First, it surveys the emerging laws of oversight, most importantly the European Union’s Artificial Intelligence Act (“AIA”). It will be shown that while the AIA is concerned with the competence of human overseers, it does not provide much guidance on how to achieve effective oversight and leaves oversight obligations for AI developers underdefined. Second, this article presents a novel taxonomy of human oversight roles, differentiated along whether human intervention is constitutive to, or corrective of a decision made or supported by an AI. The taxonomy allows to propose suggestions for improving effectiveness tailored to the type of oversight in question. Third, drawing on scholarship within democratic theory, this article formulates six normative principles which institutionalise distrust in human oversight of AI. The institutionalisation of distrust has historically been practised in democratic governance. Applied for the first time to AI governance, the principles anticipate the fallibility of human overseers and seek to mitigate them at the level of institutional design. They aim to directly increase the trustworthiness of human oversight and to indirectly inspire well-placed trust in AI governance. (shrink)

There is widespread consensus among policymakers that climate change and digitalisation constitute the most pressing global transformations shaping human life in the 21st century. Seeking to address the challenges arising at this juncture, governments, technologists and scientists alike increasingly herald artificial intelligence (AI) as a vehicle to propel climate change mitigation and adaptation. In this paper, we explore the intersection of digitalisation and climate change by examining the deployment of AI in government-led climate action. Building on participant observations conducted in (...) the context of the “Civic Tech Lab for Green”—a government-funded public interest AI initiative—and eight expert interviews, we investigate how AI shapes the negotiation of environmental sustainability as an issue of public interest. Challenging the prescribed means–end relationship between AI and environmental protection, we argue that the unquestioned investment in AI curtails political imagination and displaces discussion of climate “problems” and possible “solutions” with “technology education”. This line of argumentation is rooted in empirical findings that illuminate three key tensions in current coproduction efforts: “AI talk vs. AI walk”, “civics washing vs. civics involvement” and “public invitation vs. public participation”. Emphasising the importance of re-exploring the innovative state in climate governance, this paper extends academic literature in science and technology studies that examines public participation in climate change adaptation by shedding light on the emergent phenomenon of public interest AI. (shrink)

As the capabilities of Artificial Intelligence (AI) have increased over recent years, so have the challenges of how to govern its usage. Consequently, prominent stakeholders across academia, government, industry, and civil society have called for states to devise and deploy principles, innovative policies, and best practices to regulate and oversee these increasingly powerful AI tools. Developing a robust AI governance system requires extensive collective efforts throughout the world. It also raises old questions of politics, democracy, and administration, but with (...) the new challenges posed by AI's growing influence on markets, governing structures, international relations, healthcare, science, and political activism. The Oxford Handbook of AI Governance delineates the scope of these issues and addresses the key questions of AI governance. Across forty-nine chapters, organized in nine major sections, the Handbook covers the theoretical and ethical foundations of AI governance, different frameworks for developing a governance structure for AI, practical perspectives on AI governance in different policy domains, economic analyses of AI governance, and concrete lessons about the impact of AI governance domestically and internationally. Chapter authors come from a wide set of disciplines, areas of study, and cultural backgrounds, providing a global perspective on AI governance. (shrink)

The creation of increasingly complex artificial intelligence (AI) systems raises urgent questions about their ethical and social impact on society. Since this impact ultimately depends on political decisions about normative issues, political philosophers can make valuable contributions by addressing such questions. Currently, AI development and application are to a large extent regulated through non-binding ethics guidelines penned by transnational entities. Assuming that the global governance of AI should be at least minimally democratic and fair, this paper sets out three (...) desiderata that an account should satisfy when theorizing about what this means. We argue, first, that an analysis of democratic values, political entities and decision-making should be done in a holistic way; second, that fairness is not only about how AI systems treat individuals, but also about how the benefits and burdens of transformative AI are distributed; and finally, that justice requires that governance mechanisms are not limited to AI technology, but are incorporated into a range of basic institutions. Thus, rather than offering a substantive theory of democratic and fair AI governance, our contribution is metatheoretical: we propose a theoretical framework that sets up certain normative boundary conditions for a satisfactory account. (shrink)

The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key (...) normative choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain. These correspond to: the AI lifecycle model used; the scope of stakeholder involvement; the accountability goals chosen; the choice of auditing requirements; and the choice of mechanisms for transparency and traceability. We provide initial recommendations for navigating these tradeoffs and highlight the importance of a pro-ethical institutional culture. (shrink)

In this article, we shed light on the emergence, diffusion, and use of socio-technological future visions. The artificial intelligence future vision of the German federal government is examined and juxtaposed with the respective news media coverage of the German media. By means of a content analysis of frames, it is demonstrated how the German government strategically uses its AI future vision to uphold the status quo. The German media largely adapt the government´s frames and do not integrate alternative future narratives (...) into the public debate. These findings are substantiated in the framing of AI futures in policy documents of the German government and articles of four different German newspapers. It is shown how the German past is mirrored in the German AI future envisioned by the government, safeguarding the present power constellation that is marked by a close unity of politics and industry. The German media partly expose the government´s frames and call for future visions that include fundamentally different political designs less influenced by the power structures of the past and present. (shrink)

A major international consultancy firm identified ‘AI ethicist’ as an essential position for companies to successfully implement artificial intelligence (AI) at the start of 2019. It declares that AI ethicists are needed to help companies navigate the ethical and social issues raised by the use of AI. Top 5 AI hires companies need to succeed in 2019. The view that AI is beneficial but nonetheless potentially harmful to individuals and society is widely shared by the industry, academia, governments, and civil (...) society organizations. Accordingly and in order to realize its benefits while avoiding ethical pitfalls and harmful consequences, numerous initiatives have been established to a) examine the ethical, social, legal and political dimensions of AI and b) develop ethical guidelines and recommendations for design and implementation of AI. (shrink)

In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence. First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities can (...) support AI. At the heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations. (shrink)

New technologies with military applications may demand new modes of governance. In this article, we develop a taxonomy of technology governance forms, outline their strengths, and red-team their weaknesses. In particular, we consider the challenges and opportunities posed by advancing artificial intelligence, which is likely to have substantial dual-use properties. We conclude that subnational governance, though prevalent and mitigating some risks, is insufficient when the individual rewards from societally harmful actions outweigh normative sanctions, as is likely to (...) be the case with AI. Nationally enforced standards are promising ways to govern AI deployment, but they are less viable in the “race-to-the-bottom” environments that are becoming common. When it comes to powerful technologies with military implications, there is only one multilateral option with a strong historical precedent: a non-proliferation plus norms-of-use regime, which we call NPT+. We believe that a non-proliferation regime may, therefore, be the necessary foundation for AI governance. However, AI may exhibit characteristics that would make a non-proliferation regime less effective than it has proven for nuclear weapons. As an alternative, verification-backed restrictions on AI development and use would address more risks, but they face challenges in the case of advanced AI, and we show how these challenges may not have technical solutions. Perhaps more importantly, we show that there is no clear example of major powers restricting the development of a powerful military technology when that technology lacks a ready substitute. We, therefore, turn to a final alternative, International Monopoly, which was the preferred solution of many scholars and policymakers in the early nuclear era. It should be considered again for governing AI: a monopoly would require less-invasive monitoring, though at the possible cost of eroding national sovereignty. Ultimately, we conclude that it is too soon to tell whether a non-proliferation regime, a verification-based regime, or an International Monopoly is most feasible for governing AI. Nonetheless, a variety of policies would yield a high return across all three scenarios, and we conclude by identifying some of these steps that could be taken today. (shrink)

The recent proliferation of AI scandals led private and public organisations to implement new ethics guidelines, introduce AI ethics boards, and list ethical principles. Nevertheless, some of these efforts remained a façade not backed by any substantive action. Such behaviour made the public question the legitimacy of the AI industry and prompted scholars to accuse the sector of ethicswashing, machinewashing, and ethics trivialisation—criticisms that spilt over to institutional AI ethics boards. To counter this widespread issue, contributions in the literature have (...) proposed fixes that do not consider its systemic character and are based on a top-down, expert-centric governance. To fill this gap, we propose to make use of qualified informed lotteries: a two-step model that transposes the documented benefits of the ancient practice of sortition into the selection of AI ethics boards’ members and combines them with the advantages of a stakeholder-driven, participative, and deliberative bottom-up process typical of Citizens’ Assemblies. The model permits increasing the public’s legitimacy and participation in the decision-making process and its deliverables, curbing the industry’s over-influence and lobbying, and diminishing the instrumentalisation of ethics boards. We suggest that this sortition-based approach may provide a sound base for both public and private organisations in smart societies for constructing a decentralised, bottom-up, participative digital democracy. (shrink)

In this article I propose a theological reflection on the philosophical assumptions behind the idea that intelligent machine can be governed through ethical protocols, which may apply either to the people who develop the machines or to the machines themselves, or both. This idea is particularly relevant in the case of machines’ extreme wrongdoing, a wrongdoing that becomes an existential risk for humankind. I call this extreme wrong-doing, ‘evil.’ Thus, this article is a theological account on the philosophical assumptions behind (...) the possibility of evil machines, machines that can create an existential risk for humankind, and the ethical remedies that limit that risk. After discussing these assumptions, I argue for the ineffectiveness of these ethical remedies to prevent the possibility of evil machines. The article is an application of Romano Guardini’s thought on technology to evil machines. (shrink)

The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development (...) of policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector. (shrink)

Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and the (...) Council of Europe. Through a document analysis of official policy documents, combined with a data mining approach and an inductive thematic analysis, the study looks at how news media are dealt with, in particular regarding their responsibilities towards their users and society. The findings show that regulatory frameworks about AI rarely include media, but if they do, they associate them with issues such as disinformation, data, and AI literacy, as well as diversity, plurality, and social responsibility. (shrink)

The common “human in the loop” narrative in artificial intelligence (AI) implementation is in critical need of analysis and explanation, as Salloch and Eriksen (2024) rightfully argue. Researchers...

People’s comfort with and acceptability of artificial intelligence (AI) instantiations is a topic that has received little systematic study. This is surprising given the topic’s relevance to the design, deployment and even regulation of AI systems. To help fill in our knowledge base, we conducted mixed-methods analysis based on a survey of a representative sample of the US population (_N_ = 2254). Results show that there are two distinct social dimensions to comfort with AI: as a peer and as a (...) superior. For both dimensions, general and technological efficacy traits—locus of control, communication apprehension, robot phobia, and perceived technology competence—are strongly associated with acceptance of AI in various roles. Female and older respondents also were less comfortable with the idea of AI agents in various roles. A qualitative analysis of comments collected from respondents complemented our statistical approach. We conclude by exploring the implications of our research for AI acceptability in society. (shrink)

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal principles (...) consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted. (shrink)

This collection of quotes demonstrates the elegant simplicity of Ai Weiwei's thoughts on key aspects of his art, politics, and life. A master at communicating powerful ideas in astonishingly few words, Ai Weiwei is known for his innovative use of social media to disseminate his views. The book is organized into six categories: freedom of expression; art and activism; government, power, and moral choices; the digital world; history, the historical moment, and the future; and personal reflections. Together, these quotes span (...) some of the most revealing moments of Ai Weiwei's eventful career-from his risky investigation into student deaths in the 2008 Sichuan earthquake to his arbitrary arrest in 2011-providing a window into the mind of one of the world's most electrifying and courageous contemporary artists. Ai Weiwei is one of China's most influential and inspiring figures. Artist, architect, curator, and activist, he has been an outspoken critic of the Chinese government's stance on human rights and democracy. (shrink)

This study addresses the current lack of empirical data on the experiences and attitudes of clinical research professionals towards AI-powered clinical trial management tools. Clinical research professionals affiliated with various Swiss and international clinical research networks were invited to participate in an online survey. The survey focused on nine use cases of AI-powered clinical trial management tools. Participants were asked to share their ethical considerations, and their experiences were assessed at both the individual and institutional levels. Answers from 110 participants, (...) primarily from Swiss academic institutions, were included in the data analysis. While participants generally held positive attitudes, mixed views were also common. Governmental attitudes were widely perceived as unclear or cautious, with the associated regulatory opacity possibly contributing to the limited adoption of AI in trial management observed in this study. In addition to a clear AI governance, ethical compliance and training emerged as crucial aspects. There was also broad consensus on the necessity of informing patients about tools’ uses and of ensuring an independent certification of the respective tools. The widespread adoption of AI-powered clinical trial management tools remains distant, entangled in a net of complexities that require further empirical investigation. Ethical concerns appear to play a crucial role, driven by distrust and the absence of a clear regulatory framework. Urgent actions are needed at both institutional and governmental levels, including the establishment of a governance framework and training provision. (shrink)

Policymakers are responsible for key decisions about political governance. Usually, they are selected or elected based on experience and then supported in their decision-making by the additional counsel of subject experts. Those satisfied with this system believe these individuals – generally speaking – will have the right intuitions about the best types of action. This is important because political decisions have ethical implications; they affect how we all live in society. Nevertheless, there is a wealth of research that cautions (...) against trusting human judgment as it can be severely flawed. This paper will look at the root causes of the most common errors of human judgment before arguing – contra the instincts of many – that future AI systems could take a range of political decisions more reliably. I will argue that, if/when engineers establish ethically robust systems, governments will have a moral obligation to refer to them as a part of decision-making. (shrink)

The conventional discourse on existential risks (x-risks) from AI typically focuses on abrupt, dire events caused by advanced AI systems, particularly those that might achieve or surpass human-level intelligence. These events have severe consequences that either lead to human extinction or irreversibly cripple human civilization to a point beyond recovery. This discourse, however, often neglects the serious possibility of AI x-risks manifesting incrementally through a series of smaller yet interconnected disruptions, gradually crossing critical thresholds over time. This paper contrasts the (...) conventional "decisive AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis". While the former envisions an overt AI takeover pathway, characterized by scenarios like uncontrollable superintelligence, the latter suggests a different causal pathway to existential catastrophes. This involves a gradual accumulation of critical AI-induced threats such as severe vulnerabilities and systemic erosion of economic and political structures. The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly converge, undermining societal resilience until a triggering event results in irreversible collapse. Through systems analysis, this paper examines the distinct assumptions differentiating these two hypotheses. It is then argued that the accumulative view can reconcile seemingly incompatible perspectives on AI risks. The implications of differentiating between these causal pathways --- the decisive and the accumulative --- for the governance of AI as well as long-term AI safety are discussed. (shrink)

We apply Ken Wilber's Integral Theory to AI governance, demonstrating its ability to systematize diverse approaches in the current multifaceted AI governance landscape. By analyzing ethical considerations, technological standards, cultural narratives, and regulatory frameworks through Integral Theory's four quadrants, we offer a comprehensive perspective on governance needs. This approach aligns AI governance with human values, psychological well-being, cultural norms, and robust regulatory standards. Integral Theory’s emphasis on interconnected individual and collective experiences addresses the deeper aspects of (...) AI-related issues. Additionally, we propose using Integral Theory as a methodology for literature reviews to overcome the fragmented understanding often seen in traditional reviews of AI governance. (shrink)

Machine‐learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well‐represented populations. Faced with this dilemma (...) between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well‐represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms—showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings. (shrink)

In popular discussions, the nuances of AI are often abridged as “the algorithm”, as the specific arrangements of machine learning, deep learning and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness (...) of algorithmic systems. Perhaps in response to concerns about “the algorithm” by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness. (shrink)

This paper criticises the ideological dimension of the AI narrative. It does so by questioning the implicit assumptions behind its vision, which promises a world that automatically adapts to our desires before we even know them. These assumptions hinge on a misconception of the value of desire as residing exclusively with its fulfilment, warranting human manipulation for increased predictability. This social trajectory towards algorithmic governance, rather than delivering on the promised fulfilment, undermines our capacity to sustain the same desire (...) that it uses to justify its enterprise. (shrink)

As artificial intelligence (AI) deployment is growing exponentially, questions have been raised whether the developed AI ethics discourse is apt to address the currently pressing questions in the field. Building on critical theory, this article aims to expand the scope of AI ethics by arguing that in addition to ethical principles and design, the organizational dimension (i.e. the background assumptions and values influencing design processes) plays a pivotal role in the operationalization of ethics in AI development and deployment contexts. Through (...) the prism of critical theory, and the notions of underdetermination and technical code as developed by Feenberg in particular, the organizational dimension is related to two general challenges in operationalizing ethical principles in AI: (a) the challenge of ethical principles placing conflicting demands on an AI design that cannot be satisfied simultaneously, for which the term ‘inter-principle tension’ is coined, and (b) the challenge of translating an ethical principle to a technological form, constraint or demand, for which the term ‘intra-principle tension’ is coined. Rather than discussing principles, methods or metrics, the notion of technical code precipitates a discussion on the subsequent questions of value decisions, governance and procedural checks and balances. It is held that including and interrogating the organizational context in AI ethics approaches allows for a more in depth understanding of the current challenges concerning the formalization and implementation of ethical principles as well as of the ways in which these challenges could be met. (shrink)

In the years 2020–2021, journalists, lawyers, scholars, and civil society actors noticed an unusual spike in the refusal of francophone African immigrants in Québec, Canada. While Immigration, refugee and citizenship Canada’s systemic racism problem were already documented, the novelty appeared to be how standardized and sometimes, “nonsensical” the reasons given to many of the applicants were. This eventually prompted a lawsuit against IRCC in which it was revealed that a new piece of software called “Chinook” had been deployed since 2018, (...) approximately at the same time application refusal started spiking. Online and media-fueled speculations culminated in a broad debate about whether Chinook was an artificial intelligence-based technology. Such controversy, for instance, unfolded during parliamentary consultations in which it was finally disclosed that Chinook was not AI per se., but more of a half-baked automated interface. The present article aims to understand how the performative ambiguity of Chinook informs on the progressive becoming of the Canadian Federal state and its immigration control practices in the current Machine Learning hype. Furthermore, it seeks to understand how the interlacing of public–private partnership, in which the private sector is mobilized to produce AI-driven technology, recursively transforms the exercise of governance and governmentality. We argue, through the Chinook controversy and its unfolding, that there is a shift occurring where AI becomes ever more central in statecraft “anarchitecture” and cybernetic adaptability. (shrink)

With the intensifying shortage of care-providers and mounting financial burden of an aging population in Japan, artificial intelligence (AI) offers a potential solution through AI-driven robots, chatbots, smartphone apps, and other AI medical services. Yet Japanese acceptance of medical AI, especially patient care, largely depends on the degree of ‘humanness’ that can be integrated into intelligent technologies. As empathy is considered a core value in the practice of healthcare workers, artificially intelligent agents must have the ability to perceive human emotions (...) and respond in an affective, human-like manner. This paper presents the results of a nationwide survey in Japan 2023 about the public perception of the use of emotionalized AI (EAI) in healthcare. The sample included 2000 Japanese citizens from 20 to 69 years old with an average age of 45 years. The questionnaire included 65 items in six categories: demographic, perception of the existing healthcare system, attitude towards the use of AI, and EAI in healthcare, trust and privacy concerns and fear of AI, and seeking literacy on new technologies. The analysis of the responses demonstrates that overall, the Japanese population has a limited knowledge of AI, EAI, and smart technologies but trusts the healthcare system, physicians, and staff to use it properly, with little worries over the potential pitfalls related to data security and safety. However, they prefer the use of more human resources and are concerned about privacy and the high cost of new technology, and believe little information is being provided to them through government and media. (shrink)

This paper aims to make a proposal to govern the Peruvian State under the umbrella of management cybernetics, following the paths of the viable system model, proposed by Prof. Stafford Beer, enriched with other soft and hard systemic methodologies and technologies, to cover the soft and hard issues that are part of the complex Peruvian reality at different levels of recursion. For doing this, four defined perspectives were adopted to understand the complexity of Peru: the sectoral view, the regions view, (...) the river basins view and the macroregions view. Peru is seen as a system in focus, defining, for each of these four perspectives, the five systems that VSM has. The application of the VSM in each perspective serves to apply it in two modes: diagnosis and design, according to the respective perspective. Then an integrative analysis and reflection is done considering the four perspectives, to analyze the viability of the VSM approach in the governance of the Peruvian State to establish some conclusions and recommendations in relation to the proposal, appearing at the end of the paper. (shrink)

One of the challenges facing AI governance is the need for multiple scales. Universal human rights require a global scale. If someone asks AI if education is harmful to women, the answer should be “no” regardless of their location. But economic democratization requires local control: if AI’s power over an economy is dictated by corporate giants or authoritarian states, it may degrade democracy’s social and environmental foundations. AI democratization, in other words, needs to operate across multiple scales. Nature allows (...) the multiscale flourishing of biological systems through fractal distributions. In this paper, we show that key elements of the fractal scaling found in nature can be applied to the AI democratization process. We begin by looking at fractal trees in nature and applying similar analytics to tree representations of online conversations. We first examine this application in the context of OpenAI’s “Democratic Inputs” projects for globally acceptable policies. We then look at the advantages of independent AI ownership at local micro-levels, reporting on initial outcomes for experiments with AI and related technologies in community-based systems. Finally, we offer a synthesis of the two, micro and macro, in a multifractal model. Just as nature allows multifractal systems to maximize biodiverse flourishing, we propose a combination of community-owned AI at the micro-level, and globally democratized AI policies at the macro-level, for a more egalitarian and sustainable future. (shrink)

Calls to hold artificial intelligence to account are intensifying. Activists and researchers alike warn of an “accountability gap” or even a “crisis of accountability” in AI. Meanwhile, several prominent scholars maintain that accountability holds the key to governing AI. But usage of the term varies widely in discussions of AI ethics and governance. This chapter begins by disambiguating some different senses and dimensions of accountability, distinguishing it from neighboring concepts, and identifying sources of confusion. It proceeds to explore the (...) idea that AI operates within an accountability gap arising from technical features of AI as well as the social context in which it is deployed. The chapter also evaluates various proposals for closing this gap. I conclude that the role of accountability in AI ethics and governance is vital but also more limited than some suggest. Accountability’s primary job description is to verify compliance with substantive normative principles—once those principles are settled. Theories of accountability cannot ultimately tell us what substantive standards to account for, especially when norms are contested or still emerging. Nonetheless, formal mechanisms of accountability provide a way of diagnosing and discouraging egregious wrongdoing even in the absence of normative agreement. Providing accounts can also be an important first step toward the development of more comprehensive regulatory standards for AI. (shrink)

In this text, our question is what is the current regulatory trend in countries that are not considered central in the development of artificial intelligence, such as Brazil: a preventive approach, or an experimental approach? We will analyze the bills (PL) that are being processed in legislative houses at the state level, and at the federal level, highlighting some elements, such as: Delimitation of the object (conceptualization), fundamental principles, ethical guidelines, relationship with human work, human supervision, and guidelines for public (...) authorities. Based on these elements, we identified how the issues that drive the contemporary debate on AI have been addressed in these legislative initiatives. The results indicate that the strongest national regulation proposal mentions risks, rights, governance, economic agents; these mentions dialogue with the concerns discussed by researchers around the world, regarding the mitigation and prevention of risks, regarding the preservation of individual and collective rights, the role of the State as a regulator and supervisor, and, as was to be expected, the maintenance of economic policies that possibly tend to protect the market; proposes that the foundations of the development, implementation and use of AI have values consistent with ‘human centered AI’ (Bingley et al. 2023), but also points to the neoliberal matrix; introduces human supervision as an effective element in the AI systems cycle; the trend that we can observe in Brazil is the configuration of a regulation that harmonizes articles that favor the preventive approach, with the presence of articles that favor the experimentation approach. (shrink)

Contemporary policy debates about managing the enormous volume of online content have taken a renewed focus on upload filtering, automated detection of potentially illegal content, and other “proactive measures”. Often, policymakers and tech industry players invoke artificial intelligence as the solution to complex challenges around online content, promising that AI is a scant few years away from resolving everything from hate speech to harassment to the spread of terrorist propaganda. Missing from these promises, however, is an acknowledgement that proactive identification (...) and automated removal of user-generated content raises problems beyond issues of “accuracy” and overbreadth--problems that will not be solved with more sophisticated AI. In this commentary, I discuss how the technical realities of content filtering stack up against the protections for freedom of expression in international human rights law. As policymakers and companies around the world turn to AI for communications governance, it is crucial that we recall why legal protections for speech have included presumptions against prior censorship, and consider carefully how proactive content moderation will fundamentally re-shape the relationship between rules, people, and their speech. (shrink)

In this article, drawing upon a feminist epistemology, we examine the critical roles that philosophical standpoint, historical usage, gender, and language play in a knowledge arena which is increasingly opaque to the general public. Focussing on the language dimension in particular, in its historical and social dimensions, we explicate how some keywords in use across artificial intelligence (AI) discourses inform and misinform non-expert understandings of this area. The insights gained could help to imagine how AI technologies could be better conceptualised, (...) explained, and governed, so that they are leveraged for social good. (shrink)

Historically, the dynamic between philosophy of artificial intelligence and its practical application has been essential for the development of both, and thus the encounter between theory of AI and architectural/urban theory should be a site of considerable productivity. However, in many ways, it is not. This is due to two primary factors, one arising from each side of this encounter. First, legacies of overly-anthropomorphic models of AI permeate design discourses, where issues of how well AI can be constrained to social (...) issues of philosophy of mind exclude more foundational and cross-cultural questions about artificial intelligence as material process in the physical world that could inform new philosophical insights. Concurrently, the mobilization of investment in “Smart Cities” discourses dominates the space of application of AI at urban scale in ways that prematurely fixes solutions in the skeuomorphic image of architectural and urban conventions. To break this impasse, we held a series of think-tanks, workshops, and design charettes that brought together leading figures in philosophy of artificial intelligence, urban design, and commercial AI platforms. The goal was to re-think from first principles how alternative philosophical models of AI as a distributed, discontinuous, landscape-scale technology in a direct encounter with the applications in contexts of ecological sensing, automation’s impact on urban form, and issues of algorithmic governance. The design brief for this research establishes a generative framework for conceiving how embedded machine sensing and intelligence is already changing urban form and could change it in the future. The framework focuses on issues of urban zoning and architectural programming, data modeling and governance, platform cognition and design, and how these inform shifts in dynamics of public and private institutions. (shrink)

As artificial intelligence technologies become increasingly prominent in our daily lives, media coverage of the ethical considerations of these technologies has followed suit. Since previous research has shown that media coverage can drive public discourse about novel technologies, studying how the ethical issues of AI are portrayed in the media may lead to greater insight into the potential ramifications of this public discourse, particularly with regard to development and regulation of AI. This paper expands upon previous research by systematically analyzing (...) and categorizing the media portrayal of the ethical issues of AI to better understand how media coverage of these issues may shape public debate about AI. Our results suggest that the media has a fairly realistic and practical focus in its coverage of the ethics of AI, but that the coverage is still shallow. A multifaceted approach to handling the social, ethical and policy issues of AI technology is needed, including increasing the accessibility of correct information to the public in the form of fact sheets and ethical value statements on trusted webpages, collaboration and inclusion of ethics and AI experts in both research and public debate, and consistent government policies or regulatory frameworks for AI technology. (shrink)