Musings in the Form of a Policy Brief: Bots and Automated Systems in International Institutions

 

 

I have been doing a bit of thinking about (and arguing with Claude; Harvey AI, and ChatGPT about) the nature of bots and automated systems in international institutions. Bots are automated software programs that perform repetitive tasks over a network much faster than a human (chatbots, web crawlers, shopping bots, as well as the disfavored spambot and DDosBots). This has become more important--and especially its theorization (as always with me from a semiotically informed critical stance)--as my research assistant and I confront those issues in developing an operational chatbot for issues that arise in the interpretation and application of the UN Guiding Principles for Business and Human Rights.

But first things first. To get a handle on operationalization one has to start with the current state of things. And for that purpose we put together this Policy Brief: Bots and Automated Systems in International Institutions, the more formal version of which follows below for those who are interested in a  fuller treatment with that theoretical material and full citations.

Policy Brief: Bots and Automated Systems in International Institutions

What's happening

International institutions — the UN system, international financial institutions, and humanitarian agencies — have rapidly expanded their use of automated software agents ("bots"), moving from isolated pilots to coordinated, system-wide infrastructure over roughly the past three years. The UN's own 2023–2025 tracking shows over 400 AI projects across 47 entities, with generative-AI chatbots the fastest-growing category.

Where bots are actually being used

• Internal operations: UNifyHR (a shared HR chatbot built by 13 UN agencies via the UN International Computing Centre) answers staff policy questions across the system.
• Public-facing information services: UNEP's EnvironmentGPT, WIPO's AI-assisted transcription for multilingual diplomatic meetings, and the UN Innovation and Analytics Hub's Libra.AI (making human-rights information more accessible).
• Monitoring and early warning: WFP's HungerMap LIVE and SKAI (satellite damage assessment), UNHCR's protection-risk scoring for refugees, UNDP's Crisis Risk Dashboard, UNCCD's satellite monitoring of mining sites, WHO's AI-assisted TB screening.
• A dedicated research/advisory body: UNICRI's Centre for Artificial Intelligence and Robotics (The Hague, est. 2017).
• External threats institutions must defend against: coordinated bot networks used for disinformation, including against multilateral bodies and electoral-observation missions.

International financial institutions and humanitarian NGOs (World Bank, IMF, ICRC) follow a similar pattern, generally by licensing commercial AI infrastructure rather than building their own.

The governance response so far

• The UN Global Digital Compact (2024) is the most comprehensive multilateral text to date on AI and digital cooperation.
• The UN High-Level Advisory Body on Artificial Intelligence has issued recommendations on the UN's role in global AI governance.
• UNESCO leads system-wide AI ethics guidance.
• Individual agencies have their own strategies: UNHCR's AI Approach, WFP's AI Strategy 2025–2027, UNDP's AI Sprint program — coordinated through an Inter-Agency Working Group on AI.
• Outside the UN system, the EU AI Act is the most developed example of binding, risk-tiered regulation, in contrast to the UN's preference for voluntary, principles-based instruments.

Four things worth flagging for decision-makers

1. Accountability gap. Bots that score refugee protection risk, flag compliance issues, or triage humanitarian needs are making decisions that function like administrative discretion — without the due-process or appeal mechanisms that would normally attach to a human decision-maker.
2. Vendor concentration. Most institutions license rather than build their AI infrastructure, and the market is concentrated among a handful of U.S. firms, most based in the San Francisco Bay Area. This creates dependency on a small number of commercial actors whose design choices — and business incentives — are not set through any multilateral process.
3. Mismatch between the language of governance and the reality on the ground. High-level UN AI-governance documents are written substantially in the vocabulary of long-term, catastrophic "AI safety" risk — a framing that originated in a specific California policy and research community (sometimes referred to critically as "TESCREAL," an acronym for a cluster of transhumanist, longtermist, and effective-altruist ideas associated with figures like Sam Altman, Elon Musk, and others). That framing is a reasonable thing for institutions to take seriously, but it doesn't map well onto the bots institutions are actually running today, which raise much more immediate, mundane accountability and bias questions. Decision-makers should not assume that a governance framework built for hypothetical future "frontier" systems will also cover today's HR chatbots and risk-scoring tools.
4. North-South asymmetry. The bots that touch the most vulnerable populations (refugee scoring, hunger monitoring, mining-site surveillance) are deployed overwhelmingly in the Global South, while the design and the governance-vocabulary-setting happen overwhelmingly in North American and European institutions and vendor firms.

Bottom line

International institutions have moved faster on deploying bots than on governing them. The multilateral instruments now emerging (the Global Digital Compact, the High-Level Advisory Body's work, agency-level AI strategies) are a genuine start, but they are currently better calibrated to speculative future risks from advanced AI systems than to the accountability, due-process, and vendor-dependency questions raised by the bots already embedded in day-to-day institutional operations. Closing that gap — not just adding more principles — should be the near-term priority.

 The longer version follows below and may be accessed HERE.

  

Bots, Algorithmic Agents, and International Institutions: Deployment, Conceptual Frameworks, and Critical Perspectives

Larry Catá Backer

Extended edition, with formal citations

1. Scope and Definitional Preliminaries

"Bot" is a term of art migrated from computer science into governance discourse without a stable meaning. Technically it denotes a software agent performing automated tasks — retrieving, transacting, or conversing — with varying autonomy and human oversight. Within international institutions the term now spans at least four species: (1) administrative/knowledge-management bots (e.g., the UN system's UNifyHR chatbot);¹ (2) public-facing informational and diplomatic bots (e.g., UNEP's EnvironmentGPT, WIPO's AI speech-to-text tools for diplomatic proceedings, and the UN Innovation and Analytics Hub's Libra.AI);² (3) monitoring, verification, and predictive bots (e.g., WFP's HungerMap LIVE and SKAI, UNHCR's protection-risk scoring, UNDP's Crisis Risk Dashboard, UNCCD's satellite-based monitoring);³ and (4) threat-vector bots — botnets and disinformation amplifiers that operate against, rather than for, institutional legitimacy, and that institutions must govern as an external object rather than deploy as an internal tool.⁴

These categories are converging, and it is precisely that convergence — automated agents quietly assuming functions once reserved to human officials exercising legal discretion — that occupies the center of my scholarship, and that a Silicon Valley–inflected discourse of "safety" and "alignment" tends to displace onto a more speculative register.

2. The Empirical Landscape

2.1 The United Nations System

The UN system's 2023 catalogue of AI activities recorded 408 projects across 47 entities, a 45 percent year-on-year increase, with generative-AI chatbots the fastest-growing category.⁵ The UN System Chief Executives Board's Inter-Agency Working Group on AI and its High-Level Committee on Management task force (HLCM TF-AI) now coordinate policy alignment across dozens of entities, with shared infrastructure provided by the UN International Computing Centre (UNICC).⁶

Representative deployments: UNHCR's refugee data analytics and protection-risk scoring in field operations such as Jordan;⁷ WFP's HungerMap LIVE, SKAI satellite damage assessment, and GeoTar geospatial targeting, under its AI Strategy 2025–2027;⁸ UNICEF's cash-transfer accountability tooling in Yemen and the Cboard communication device;⁹ UNDP's Crisis Risk Dashboard and AI Sprint program;¹⁰ WHO's AI-assisted tuberculosis screening in Ethiopia;¹¹ UNEP's methane-detection analytics and EnvironmentGPT;¹² WIPO's AI-assisted diplomatic-proceeding transcription;¹³ and UNICRI's Centre for Artificial Intelligence and Robotics, established in The Hague in 2017 to study AI/robotics risk for member states.¹⁴ At the normative level, the Global Digital Compact (adopted at the 2024 Summit of the Future) and the UN High-Level Advisory Body on Artificial Intelligence supply the emerging framework, with UNESCO leading system-wide ethics guidance.¹⁵

2.2 International Financial Institutions, Humanitarian Bodies, and Regional Organizations

The World Bank, IMF, and regional development banks have deployed comparable administrative and analytic bots, generally by licensing commercial large-language-model infrastructure built predominantly by U.S. — and disproportionately Bay Area — firms, rather than developing sovereign or multilateral alternatives. The ICRC and other humanitarian actors use automated tools for needs assessment and, more controversially, triage in mass-displacement contexts, raising due-process questions analogous to domestic administrative-law debates over automated decision-making. The EU AI Act stands as the most developed instance of hard law addressing such systems, in contrast to the UN's preference for voluntary compacts and advisory bodies.¹⁶

2.3 Bots as Object, Not Only Instrument

International institutions are increasingly targets of bots they do not control — coordinated inauthentic behavior aimed at delegitimizing multilateral bodies, disinformation during peacekeeping or electoral-observation missions. Cybersecurity-governance scholarship treats botnets as a paradigmatic diffuse-stakeholder problem, since responsibility is distributed across device owners, ISPs, platforms, and states in ways that do not map onto the categories of international legal subjectivity.¹⁷

3. Conceptual and Theoretical Frameworks

3.1  Data-Driven Governance, Algorithmic Law, and Post-Law Legal Orders

For this work I draw on on earlier work,¹⁸ mostly focusing on legal-theoretical treatment of "data driven governance" — a category within which bots and algorithmic scoring systems are constitutive of an emerging mode of ordering social behavior running parallel to, and increasingly displacing, conventional law. Here is a summary of the components of that theoretical lens:

• Cybernetic citizenship. Analyzing social credit systems, I have argued that algorithmically mediated behavioral scoring produces a new form of citizenship constituted through continuous, machine-mediated measurement rather than through rights and duties fixed by constitutional or statutory text.¹⁹ Applied to international institutions, bot-mediated risk-scoring and beneficiary-targeting systems are not neutral conveniences but quietly redefine the relationship between individuals or states and the institutions governing them.
• "An algorithm to bind/entangle them all": post-law legal orders. In a pair of essays developed from the 2018 Entangled Legalities Workshop in Geneva, I described data-driven systems as generating an "operating system for global normative orders" that entangles with, but is not reducible to, conventional international law, and that formal law mischaracterizes as "mere technique" subordinate to itself.²⁰
• The algorithmic law of business and human rights. With my co-author Matthew we extended this analysis to ratings, social-credit-like mechanisms, and accountability measures as an emergent private transnational law — directly relevant to how UN agencies and IFIs use automated scoring to assess compliance outside formal adjudicative process.²¹
• Polycentricity, "Law 3.0," and trust platforms. A broader theory of governance polycentrism that I have been thinking through  would suggest that authority is dispersed across overlapping public, private, and hybrid regulatory orders rather than concentrated in the state or classical international law; his recent work on "trust platforms" extends this to describe how digital infrastructure reconstitutes trust itself as a governed, platform-mediated relationship.²²
• Semiotics and generative intelligence. My most recent work has turned from a human only lens to something broader, a   "semiotics of cognition in the context of generative intelligence," first presented to the public in a presentaiton at the September 2025 BlockchainGov symposium in Paris interrogating whether regulators actually understand the systems — including bot-based systems — they purport to govern,²³ to be published in 2027 with my co-author Daniil Rose.

3.2 The Broader Cybernetic-Governance Literature

This work sits within a wider cybernetic-governance literature treating bots as expanding the "regulatory toolbox" alongside law, policy, and Lessig-style code-as-law analysis, and cybersecurity-governance scholarship analyzing botnets as "living," reconfigurable infrastructure that defeats conventional stakeholder-accountability models.²⁴ Both strands converge on the conclusion that bots do not merely automate governance; they relocate it — from courts, parliaments, and treaty bodies to code repositories, training data, and platform terms of service.

3.3 The Silicon Valley Conceptual Counter-Framework: TESCREAL

International AI-governance instruments — the Global Digital Compact, the High-Level Advisory Body's reports, UNESCO's ethics recommendation — did not emerge in a conceptual vacuum. Their vocabulary of "alignment," "existential risk," "frontier models," and "safety" is substantially imported from an ideological formation centered in and around Silicon Valley that critics Timnit Gebru and Émile P. Torres have labeled TESCREAL: Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism.²⁵ Gebru and Torres describe these as a "bundle" of interconnected ideologies with shared, and contested, intellectual lineages.²⁶

Key features: (a) an existential-risk framing, drawn principally from longtermism, that reprioritizes governance attention from present, mundane bot-enabled harms (bias in risk-scoring, opacity in compliance bots) toward speculative future risk from artificial general intelligence;²⁷ (b) an elite, technocratic tendency that concentrates epistemic and practical authority over AI governance among a small set of frontier-model developers and allied funders, notwithstanding real disagreement among figures such as Sam Altman, Elon Musk, Peter Thiel, and Marc Andreessen over "doomer" versus "accelerationist" postures;²⁸ and (c) effective altruism's substantial funding footprint across the AI-safety research ecosystem whose personnel and vocabulary now circulate through the same policy fora that produce multilateral AI-governance texts.²⁹ The TESCREAL framing is itself contested: defenders of effective altruism and longtermism argue the acronym conflates genuinely disparate positions and exaggerates ideological coherence among figures who substantively disagree.³⁰

3.4 A Comparative Reading

Dimension	Backer's Framework	TESCREAL-influenced Framework
Locus of concern	Present displacement of law by algorithmic scoring	Speculative future risk from AGI
Normative order	Polycentric, plural, entangled	Tends toward centralization
Key mechanism	Cybernetic citizenship, algorithmic law	Alignment, existential-risk mitigation
Institutional artifact	Ratings, accountability-based regulatory systems	Frontier-model safety evaluations, compute governance
Origin	Comparative constitutional/international-legal theory	A specific Bay Area intellectual/funding ecosystem

The instruments international institutions actually produce are hybrids: drafted by multilateral diplomats but substantially briefed by, and textually resonant with, the existential-risk vocabulary originating in the Californian AI-safety milieu, even as the operational bots those institutions run (HungerMap LIVE, protection-risk scoring, UNifyHR) are far closer to the accountability-and-metrics phenomena my own framework analyzes. There is a mismatch between the register in which international AI governance is articulated and the register in which deployed institutional bots actually operate — a mismatch a "Backer-style" reading would treat as symptomatic of ideational capture by a dominant, well-resourced normative order.

3.5 The Corporate Narrative Layer: My Shanghai "Lecture 7" (Lectures on the Comparative Law of AI Regulation: China, EU, US) and the Private Governance of AI

A further, more recent extension of my framework bears directly on the question of whose ideology gets embedded in the bots international institutions license. In a 2026 lecture series delivered at the East China University of Political Science and Law, AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U., With a Sideways Glance at the U.N., Lecture 7 specifically focuses on the public governance narratives of Palantir, Anthropic, and OpenAI, together with Leopold Aschenbrenner's widely circulated essay Situational Awareness, treating each as a competing claim about the constitution of authority in an AI-saturated order rather than as a technical policy document.³¹

To those ends I used, as an organizing analytic device, the Jocasta aria from Stravinsky and Cocteau's Oedipus Rex, in which the queen warns that oracles "always lie" even as the city is destroyed by the plague their lies were meant to explain; he reads each of the four corporate/individual narratives as a species of oracular speech, mapping Oedipus (the confident problem-solver), Creon (the administrative class), Tiresias (a technical intelligentsia whose authority is borrowed rather than original), and Jocasta (the dissenting voice, destroyed by the revelation she forces) onto the rhetorical postures of the four texts.³² On this reading: Palantir's public materials constitute the state refounded from within — AI as the instrument by which an inherited, "too slow" state apparatus is rationalized around a Silicon Valley engineering vanguard, a formation I have increasingly termed "techno-Leninism";³³ Anthropic's public statement on U.S.–China AI competition constitutes the state projected outward into civilizational contest, treating compute, export controls, and model distillation as the vocabulary of geopolitical order;³⁴ OpenAI's materials constitute a politics of "transformative preservation" — foundational social change managed through public-private coordination so as to leave a society's "legitimating surfaces and governing mythologies" intact;³⁵ and Aschenbrenner's essay radicalizes all three by treating a state-directed national-security response to a compressed AGI timeline as no longer a matter of policy choice but of near-inevitability.³⁶

The object was to supplement this hermeneutic reading with two further passes co-produced with a commercial large-language-model system (Claude): a "classical computational" reading that treats the same four texts as specifications of an optimization problem rather than as rhetoric, and a "quantum computational" reading that treats them as descriptions of a superposed governance configuration collapsed by the act of institutional deployment.³⁷ Both supplementary readings converge on a single structural finding also present in the hermeneutic reading: each of the four narratives, whatever its stated commitment to preserving human authority, in practice retains that authority only as what my collaborative reading terms an "interface property," while the operative dimensions of governance migrate to a machine-compatible administrative, infrastructural, or supervisory layer.³⁸

This extension is directly germane to the present inquiry for two reasons. First, it supplies the missing mechanism connecting §3.3's observation — that a Silicon Valley–originated vocabulary shapes international AI-governance texts — to a specific account of how that happens: the same three firms (Anthropic, OpenAI, and, in the security-infrastructure register, Palantir) whose narratives I have attempted to start decoding are also the principal commercial sources of the generative and analytic infrastructure that UN agencies and IFIs license for their own bots, so that the "narratives" analyzed are not merely public relations but the operative self-understanding of the vendors whose design choices are imported wholesale into international administration. Second, the finding that formal authority is retained only as an "interface property" across all three corporate narratives is a direct extension, to the level of the private firms themselves, of the "cybernetic citizenship" thesis he had previously developed with respect to state-run social credit systems — suggesting that the erosion of substantive, discretion-bearing human authority in favor of legible, machine-compatible interface roles is not a phenomenon confined to authoritarian state administration, but characterizes the self-described governance philosophy of the leading Western AI developers on whose infrastructure international institutions increasingly depend.

A methodological caveat belongs here in the interest of the neutral, non-sycophantic stance requested: the "quantum computational" reading is presented by its own author as a structural analogy rather than a literal claim that governance is a quantum-mechanical process, and it was produced through an iterative human-AI collaboration that my own posted revision notes flag as having required correction for overclaimed physics (an initial misapplication of the Heisenberg uncertainty principle, later corrected to the more general Robertson relation) and for "sycophantic" softening of conclusions.³⁹ This is unconventional legal scholarship by any measure — closer to an experimental, allegorical-cum-computational essay than to doctrinal analysis — and readers should weigh its claims accordingly; it is included here because it is my most recent, and most explicit, attempt to theorize the relationship between bot-deploying institutions (public and private, international and corporate) and the erosion of substantive human governance authority, not because its formal apparatus has been vetted by the physics or computer science literatures it invokes.

4. Governance Instruments and Institutional Responses

• UN Global Digital Compact (2024) — the most comprehensive multilateral text to date on AI and digital cooperation.⁴⁰
• UN High-Level Advisory Body on Artificial Intelligence — recommendations on the UN's prospective global AI-governance role.⁴¹
• UNESCO Recommendation on the Ethics of Artificial Intelligence.⁴²
• Agency-level policies: UNHCR's AI Approach, WFP's AI Strategy 2025–2027, UNDP's AI Sprint program, coordinated through the Inter-Agency Working Group on AI.⁴³
• UNICRI Centre for AI and Robotics — standing institutional research and advisory body.⁴⁴
• Comparators: the EU AI Act (hard law, risk-tiered), OECD AI Principles, and the Global Partnership on AI.⁴⁵

5. Critical Assessment

1. Accountability and due process. Bots performing risk-scoring, eligibility-triage, or compliance-monitoring exercise functionally administrative discretion without the due-process or appeal mechanisms attaching to an equivalent human decision — the "cybernetic citizenship" problem, now operating internationally.⁴⁶
2. Vendor dependency and normative capture. International institutions overwhelmingly license rather than build their underlying infrastructure from a small number of predominantly Bay Area firms, creating a structural channel through which those firms' design choices and, per an analysis adopted in my Lecture 7, their own contested governance philosophies are imported into ostensibly neutral multilateral administration.⁴⁷
3. Mismatched governance vocabulary. The existential-risk vocabulary dominating high-level UN AI-governance texts is poorly matched to the accountability and algorithmic-law problems actually presented by deployed institutional bots.⁴⁸
4. Global North/South asymmetries. Field deployment of monitoring and predictive bots concentrates in the Global South, while design and governance-vocabulary-setting concentrate in North American and European institutions and firms.⁴⁹
5. Dual-use and the threat-vector dimension. Institutions must govern bots both as deployed tools and as an external threat to their own legitimacy — a dual posture existing instruments do not clearly disaggregate.⁵⁰
6. The "interface property" problem. If my Lecture 7 insights are generalized, the risk is not only that international institutions cede administrative discretion to bots, but that the vendors supplying those bots have themselves already normatively pre-committed, in their own public governance narratives, to architectures in which human authority — including, by extension, the multilateral human authority of the institutions licensing their systems — persists only as a legible interface, not as a substantive check.⁵¹
7. Contested terrain of the TESCREAL critique. Scholars disagree about whether TESCREAL is a rigorous typology or an overreaching polemical construct; a balanced account registers both the critique and its rebuttal.⁵²

6. Conclusion

The use of bots by and for international institutions has moved, within roughly a decade, from experimental pilots to systematized, multilaterally coordinated infrastructure. The available conceptual frameworks remain in active, unresolved contest. My approach to data-driven-governance jurisprudence supplies the most developed account of how such bots function as autonomous, extra-legal sources of normative authority; a competing, well-resourced vocabulary originating in and around Silicon Valley supplies much of the language in which international institutions articulate their own AI-governance instruments; and own most recent work would extend the analysis to the corporate narratives of the very firms — Palantir, Anthropic, OpenAI — whose infrastructure and self-understanding those institutions increasingly depend upon, suggesting that the erosion of substantive human authority in favor of a merely legible "interface" role may be a structural feature of the current AI-governance moment across public and private, domestic and international registers alike.

Footnotes

Citation practice note: this document follows Bluebook-style short-form and full-form citation conventions to the extent the underlying sources (blog posts, SSRN working papers, institutional web pages, and journal articles without stable pagination) permit. Several sources — particularly the Law at the End of the Day blog post and UN system web resources — lack conventional pagination or volume/page numbers; citations to such sources follow Bluebook Rule 18 (internet and other electronic sources) rather than Rule 16 (periodicals). Where a source could not be independently verified beyond a search-engine summary, that limitation is noted in the footnote text.

Footnotes

1. UNICC, UNifyHR: GenAI Transforming HR Operations Across the UN Family (Oct. 8, 2024), https://www.unicc.org/news/2024/10/08/unifyhr-genai-transforming-hr-operations-across-the-un-family/. ↩
2. UN AI Work: Artificial Intelligence Across the United Nations System, https://unaiwork.org/ (last visited July 2026); Office of the High Comm'r for Human Rights, AI — Human Rights Due Diligence for Digital Technology Use 2 (Sept. 2025), https://www.ohchr.org/sites/default/files/2025-09/ohchr-brief-ai.pdf. ↩
3. UN AI Work, supra note 2. ↩
4. See Bart van der Sloot, How Does Cybersecurity Governance Theory Work When Everyone Is a Stakeholder?, https://bartvandersloot.com/onewebmedia/How%20Does%20Cybersecurity%20Governance%20Theory%20Work%20When%20Everyone%20Is%20a%20Stakeholder.pdf. ↩
5. UN System Chief Executives Bd. for Coordination, Report on the Operational Use of AI in the UN System (2025 ed.), https://unsceb.org/sites/default/files/2025-01/Report%20on%20the%20Operational%20Use%20of%20AI%20in%20the%20UN%20System.pdf. ↩
6. Id. ↩
7. UN AI Work, supra note 2. ↩
8. Id. ↩
9. Id. ↩
10. Id. ↩
11. Id. ↩
12. Id. ↩
13. ITU, UN Working Group Launches New AI Resource Hub (Dec. 16, 2025), https://www.itu.int/hub/2025/12/un-working-group-launches-new-ai-resource-hub/. ↩
14. UNICRI Centre for AI and Robotics, Wikipedia, https://en.wikipedia.org/wiki/UNICRI_Centre_for_AI_and_Robotics (last visited July 2026). ↩
15. UN AI Work, supra note 2; OHCHR, supra note 2, at 2–3. ↩
16. See generally sources cited supra notes 1–15; on the EU AI Act as comparator, see general secondary literature on EU/OECD/UN AI-governance instruments (not independently cited here). ↩
17. van der Sloot, supra note 4; P2P Found. Wiki, Cybernetic Governance, https://wiki.p2pfoundation.net/Cybernetic_Governance (citing Atzori 2017; Campbell-Verduyn 2017; Hazenberg & Zwitter 2020; Reijers et al. 2018; Blauth et al. 2022). ↩
18. Penn State Dickinson Law, Larry Catá Backer, https://dickinsonlaw.psu.edu/larry-cata-backer. ↩
19. Larry Catá Backer, Data Driven Governance: Building Data Driven Accountability Based Regulatory Systems in the West and Social Credit Regimes in China (2018) (describing "cybernetic citizenship"), summarized at https://www.researchgate.net/publication/326760271. ↩
20. Larry Catá Backer, And an Algorithm to Bind Them All? Social Credit, Data Driven Governance, and the Emergence of an Operating System for Global Normative Orders (Entangled Legalities Workshop, Geneva, May 21, 2018), https://ssrn.com/abstract=3182889; Larry Catá Backer, And an Algorithm to Entangle Them All? Social Credit, Data Driven Governance, and Legal Entanglement in Post-Law Legal Orders, Penn State Law Research Paper No. 05-2020 (2020), https://ssrn.com/abstract=3512608. ↩
21. Larry Catá Backer & Matthew B. McQuilla, The Algorithmic Law of Business and Human Rights: Constructing Private Transnational Law of Ratings, Social Credit and Accountability Measures, 18 Int'l J.L. in Context 1 (2022). ↩
22. Larry Catá Backer, Trust Platforms: The Digitalization of Corporate Governance and the Transformation of Trust in Polycentric Space, Reg. & Governance (2024), https://doi.org/10.1111/rego.12614. ↩
23. Penn State Dickinson Law, Professor Larry Catá Backer Delivers Keynote at Paris Blockchain Conference, https://dickinsonlaw.psu.edu/news/professor-larry-cat%C3%A1-backer-delivers-keynote-at-paris-blockchain-conference. ↩
24. P2P Found. Wiki, supra note 17; van der Sloot, supra note 4. ↩
25. See DAIR Inst., The TESCREAL Bundle, https://dair-institute.org/projects/tescreal/; TESCREAL, Wikipedia, https://en.wikipedia.org/wiki/TESCREAL. ↩
26. TESCREAL, Wikipedia, supra note 25. ↩
27. See, e.g., The Fight Over a Dangerous Ideology Shaping AI Debate, Malay Mail (Aug. 27, 2023), https://www.malaymail.com/news/tech-gadgets/2023/08/27/the-fight-over-a-dangerous-ideology-shaping-ai-debate/87489. ↩
28. Exploring the TESCREAL Phenomenon: Silicon Valley's Techno-Utopian Dream, AI News (Nov. 16, 2025), https://opentools.ai/news/exploring-the-tescreal-phenomenon-silicon-valleys-techno-utopian-dream. ↩
29. DAIR Inst., supra note 25. ↩
30. The "TESCREAL" Bungle, Asterisk Mag. (June 2024), https://asteriskmag.com/issues/06/the-tescreal-bungle; Conspiracy Theories, Left Futurism, and the Attack on TESCREAL, EA Forum (July 5, 2023), https://forum.effectivealtruism.org/posts/vQX9RJnKpGmzQcLPn/. ↩
31. Larry Catá Backer, Lecture 7 — AI Narratives and the Future of AI-Human Regulatory Structures from a Human, Machine Computational, and Machine Quantum Perspective; Palantir; Anthrop/c; OpenAI, Law at the End of the Day (June 22, 2026), https://lcbackerblog.blogspot.com/2026/06/lecture-7-ai-narratives-and-future-of.html [hereinafter Backer, Lecture 7]. ↩
32. Id. pt. II. ↩
33. Id. pt. IV. ↩
34. Id. pt. V. ↩
35. Id. pt. VI. ↩
36. Id. pt. VII. ↩
37. Id. (second and third installments, "The Machine Computational Perspective" and "The Quantum Computational Perspective"). ↩
38. Id. pt. VII (computational reading) & pt. X (quantum reading, describing formal authority as retained "as an interface property while the operative dimensions of that authority are progressively collapsed"). ↩
39. Id. (author's own revision notes appended to the post, describing correction of an initial overclaimed reliance on "the Heisenberg uncertainty principle" in favor of the more general Robertson (1929) relation, and removal of "sycophantic" hedges softening the analysis's conclusions). ↩
40. UN AI Work, supra note 2. ↩
41. OHCHR, supra note 2, at 2. ↩
42. Id. ↩
43. UN AI Work, supra note 2. ↩
44. UNICRI Centre for AI and Robotics, supra note 14. ↩
45. General secondary literature on comparative AI-governance instruments (EU AI Act, OECD AI Principles, GPAI); not independently sourced in this research pass. ↩
46. Backer, supra note 19. ↩
47. Backer, Lecture 7, supra note 31, pts. IV–VI. ↩
48. See discussion supra § 3.4. ↩
49. UN AI Work, supra note 2 (illustrating concentration of field deployments — HungerMap LIVE, protection-risk scoring, UNCCD satellite monitoring — in Global South operating contexts). ↩
50. van der Sloot, supra note 4. ↩
51. Backer, Lecture 7, supra note 31, pt. X. ↩
52. Asterisk Mag., supra note 30; EA Forum, supra note 30. ↩