Paper by Marion Fourcade and Jeff Gordon: “What does it mean to sense, see, and act like a state in the digital age? We examine the changing phenomenology, governance, and capacity of the state in the era of big data and machine learning. Our argument is threefold. First, what we call the dataist state may be less accountable than its predecessor, despite its promise of enhanced transparency and accessibility. Second, a rapid expansion of the data collection mandate is fueling a transformation in political rationality, in which data affordances increasingly drive policy strategies. Third, the turn to dataist statecraft facilitates a corporate reconstruction of the state. On the one hand, digital firms attempt to access and capitalize on data “minted” by the state. On the other hand, firms compete with the state in an effort to reinvent traditional public functions. Finally, we explore what it would mean for this dataist state to “see like a citizen” instead…(More)”.
A standardised differential privacy framework for epidemiological modeling with mobile phone data
Paper by Merveille Koissi Savi et al: “During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework…(More)”.
AI is Like… A Literature Review of AI Metaphors and Why They Matter for Policy
Paper by Matthijs M. Maas: “As AI systems have become increasingly capable and impactful, there has been significant public and policymaker debate over this technology’s impacts—and the appropriate legal or regulatory responses. Within these debates many have deployed—and contested—a dazzling range of analogies, metaphors, and comparisons for AI systems, their impact, or their regulation.
This report reviews why and how metaphors matter to both the study and practice of AI governance, in order to contribute to more productive dialogue and more reflective policymaking. It first reviews five stages at which different foundational metaphors play a role in shaping the processes of technological innovation, the academic study of their impacts; the regulatory agenda, the terms of the policymaking process, and legislative and judicial responses to new technology. It then surveys a series of cases where the choice of analogy materially influenced the regulation of internet issues, as well as (recent) AI law issues. The report then provides a non-exhaustive survey of 55 analogies that have been given for AI technology, and some of their policy implications. Finally, it discusses the risks of utilizing unreflexive analogies in AI law and regulation.
By disentangling the role of metaphors and frames in these debates, and the space of analogies for AI, this survey does not aim to argue against the use or role of analogies in AI regulation—but rather to facilitate more reflective and productive conversations on these timely challenges…(More)”.
AI Adoption in America: Who, What, and Where
Paper by Kristina McElheran: “…We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more-educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups’ adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist…(More)”.
Why Deliberation and Voting Belong Together
Paper by Simone Chambers & Mark E. Warren: “The field of deliberative democracy now generally recognizes the co-dependence of deliberation and voting. The field tends to emphasize what deliberation accomplishes for vote-based decisions. In this paper, we reverse this now common view to ask: In what ways does voting benefit deliberation? We discuss seven ways voting can complement and sometimes enhance deliberation. First, voting furnishes deliberation with a feasible and fair closure mechanism. Second, the power to vote implies equal recognition and status, both morally and strategically, which is a condition of democratic deliberation. Third, voting politicizes deliberation by injecting the strategic features of politics into deliberation—effectively internalizing conflict into deliberative processes, without which they can become detached from their political environments. Fourth, anticipation of voting may induce authenticity by revealing preferences, as what one says will count. Fifth, voting preserves expressions of dissent, helping to push back against socially induced pressures for consensus. Sixth, voting defines the issues, such that deliberation is focused, and thus more likely to be effective. And, seventh, within contexts where votes are public—as in representative contexts, voting can induce accountability, particularly for one’s claims. We then use these points to discuss four general types of institutions—general elections, legislatures, minipublics, and minipublics embedded in referendum processes—that combine talking and voting, with the aim of identifying designs that do a better or worse job of capitalizing upon the strengths of each…(More)”.
Towards an Inclusive Data Governance Policy for the Use of Artificial Intelligence in Africa
Paper by Jake Okechukwu Effoduh, Ugochukwu Ejike Akpudo and Jude Dzevela Kong: “This paper proposes five ideas that the design of data governance policies for the inclusive use of artificial intelligence (AI) in Africa should consider. The first is for African states to carry out an assessment of their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance which involves data processing practices that protect security of personal data and privacy of data subjects; ensures that personal data is processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity and Personal Data Protection. The fourth is for states to be critical about the extent that AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensuring a transparent procurement process for AI systems from abroad where no local options exist…(More)”
Addressing ethical gaps in ‘Technology for Good’: Foregrounding care and capabilities
Paper by Alison B. Powell et al: “This paper identifies and addresses persistent gaps in the consideration of ethical practice in ‘technology for good’ development contexts. Its main contribution is to model an integrative approach using multiple ethical frameworks to analyse and understand the everyday nature of ethical practice, including in professional practice among ‘technology for good’ start-ups. The paper identifies inherent paradoxes in the ‘technology for good’ sector as well as ethical gaps related to (1) the sometimes-misplaced assignment of virtuousness to an individual; (2) difficulties in understanding social constraints on ethical action; and (3) the often unaccounted for mismatch between ethical intentions and outcomes in everyday practice, including in professional work associated with an ‘ethical turn’ in technology. These gaps persist even in contexts where ethics are foregrounded as matters of concern. To address the gaps, the paper suggests systemic, rather than individualized, considerations of care and capability applied to innovation settings, in combination with considerations of virtue and consequence. This paper advocates for addressing these challenges holistically in order to generate renewed capacity for change at a systemic level…(More)”.
Democratic self-government and the algocratic shortcut: the democratic harms in algorithmic governance of society
Paper by Nardine Alnemr: “Algorithms are used to calculate and govern varying aspects of public life for efficient use of the vast data available about citizens. Assuming that algorithms are neutral and efficient in data-based decision making, algorithms are used in areas such as criminal justice and welfare. This has ramifications on the ideal of democratic self-government as algorithmic decisions are made without democratic deliberation, scrutiny or justification. In the book Democracy without Shortcuts, Cristina Lafont argued against “shortcutting” democratic self-government. Lafont’s critique of shortcuts turns to problematise taken-for-granted practices in democracies that bypass citizen inclusion and equality in authoring decisions governing public life. In this article, I extend Lafont’s argument to another shortcut: the algocratic shortcut. The democratic harms attributable to the algocratic shortcut include diminishing the role of voice in politics and reducing opportunities for civic engagement. In this article, I define the algocratic shortcut and discuss the democratic harms of this shortcut, its relation to other shortcuts to democracy and the limitations of using shortcuts to remedy algocratic harms. Finally, I reflect on remedy through “aspirational deliberation”…(More)”.
When is a Decision Automated? A Taxonomy for a Fundamental Rights Analysis
Paper by Francesca Palmiotto: “This paper addresses the pressing issues surrounding the use of automated systems in public decision-making, with a specific focus on the field of migration, asylum, and mobility. Drawing on empirical research conducted for the AFAR project, the paper examines the potential and limitations of the General Data Protection Regulation and the proposed Artificial Intelligence Act in effectively addressing the challenges posed by automated decision making (ADM). The paper argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications, where automated systems assist human decision-makers rather than replace them entirely. This discrepancy between the legal framework and practical implementation highlights the need for a fundamental rights approach to legal protection in the automation age. To bridge the gap between ADM in law and practice, the paper proposes a taxonomy that provides theoretical clarity and enables a comprehensive understanding of ADM in public decision-making. This taxonomy not only enhances our understanding of ADM but also identifies the fundamental rights at stake for individuals and the sector-specific legislation applicable to ADM. The paper finally calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society…(More)”.
The growing energy footprint of artificial intelligence
Paper by Alex de Vries: “Throughout 2022 and 2023, artificial intelligence (AI) has witnessed a period of rapid expansion and extensive, large-scale application. Prominent tech companies such as Alphabet and Microsoft significantly increased their support for AI in 2023, influenced by the successful launch of OpenAI’s ChatGPT, a conversational generative AI chatbot that reached 100 million users in an unprecedented 2 months. In response, Microsoft and Alphabet introduced their own chatbots, Bing Chat and Bard, respectively.
This accelerated development raises concerns about the electricity consumption and potential environmental impact of AI and data centers. In recent years, data center electricity consumption has accounted for a relatively stable 1% of global electricity use, excluding cryptocurrency mining. Between 2010 and 2018, global data center electricity consumption may have increased by only 6%.
There is increasing apprehension that the computational resources necessary to develop and maintain AI models and applications could cause a surge in data centers’ contribution to global electricity consumption.
This commentary explores initial research on AI electricity consumption and assesses the potential implications of widespread AI technology adoption on global data center electricity use. The piece discusses both pessimistic and optimistic scenarios and concludes with a cautionary note against embracing either extreme…(More)”.