Sources
The 330 papers, reports and official documents that the claims rest on, grouped by debate. Each source page links to the original where a link has been checked, and lists the claims that cite it.
- LD-01: What kind of thing is generative AI, and does the answer predict anything? (35)
- LD-02: Aggregate null or distributional shift? Employment (18)
- LD-03: Organizational absorption: Why micro gains do not become macro outcomes (33)
- LD-04: Levelling or hollowing? Skill compression and the reproduction of expertise (29)
- LD-05: Discretion curtailed, relocated or encoded? (20)
- LD-06: The evaluation apparatus: Can benchmarks, leaderboards and audits carry the weight placed on them? (29)
- LD-07: AI in the research apparatus (29)
- LD-08: Who can know about AI? Epistemic dependence and jurisdiction over the field (37)
- LD-09: Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike? (34)
- LD-10: Persuasion, Intermediation & the Information Order (27)
- LD-11: Concentration, Stack & Sovereignty (25)
- LD-12: Regulation-in-Practice: Rights, Compliance & Developmental Steering (25)
LD-01: What kind of thing is generative AI, and does the answer predict anything?
- Anthropic, “Building effective agents,” Anthropic engineering post, 19 December 2024, as served on 16 October 2026
- Bank for International Settlements, “Artificial intelligence and the economy: implications for central banks,” Annual Economic Report 2024, chapter III
- Bank for International Settlements, BIS Quarterly Review, December 2025, overview (“Volatility challenges risk-taking”)
- Berglund, Tong, Kaufmann, Balesni, Cooper Stickland, Korbak & Evans, “The Reversal Curse: LLMs trained on ‘A is B’ fail to learn ‘B is A’,” ICLR 2024
- Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” NBER Working Paper 32966, 2024 (revised February 2025)
- Bommasani et al., “On the Opportunities and Risks of Foundation Models,” arXiv:2108.07258, 2021
- Chan et al., “Harms from Increasingly Agentic Algorithmic Systems,” FAccT 2023
- Chelikavada & Bennett, “Examining the Relationship between Scientific Publishing Activity and Hype-Driven Financial Bubbles: A Comparison of the Dot-Com and AI Eras,” arXiv:2509.11982v2, 2025
- Colombatto & Fleming, “Folk psychological attributions of consciousness to large language models,” Neuroscience of Consciousness, 2024
- Dziri et al., “Faith and Fate: Limits of Transformers on Compositionality,” NeurIPS 2023
- Eloundou, Manning, Mishkin & Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,” arXiv:2303.10130, 2023
- Esposito, Artificial Communication: How Algorithms Produce Social Intelligence, MIT Press, 2022
- European Commission, “Guidelines on the scope of the obligations for providers of general-purpose AI models established by Regulation (EU) 2024/1689 (AI Act),” C(2025) 7719 final, 19 November 2025
- Fenoglio, “Asymmetric Communication: Large Language Models and Language Games,” arXiv:2607.28137, 2026
- Gurnee & Tegmark, “Language Models Represent Space and Time,” ICLR 2024
- Kasirzadeh & Gabriel, “Characterizing AI Agents for Alignment and Governance,” arXiv:2504.21848, 2025
- Kolt, “Governing AI Agents,” arXiv:2501.07913, 2025 (forthcoming in the Notre Dame Law Review)
- Krokowski & Hirsch-Kreinsen, “Once promised, forever believed? An essay on the blind spots of AI promises,” AI & Society, 2025
- Kwa et al., “Measuring AI Ability to Complete Long Software Tasks,” arXiv:2503.14499v4, 2026
- Li, Hopkins, Bau, Viégas, Pfister & Wattenberg, “Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task,” ICLR 2023
- Luitse & Denkena, “The great Transformer: Examining the role of large language models in the political economy of AI,” Big Data & Society, 2021
- McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023
- McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024
- METR, “Time Horizon 1.1,” METR blog post, 29 January 2026
- Millière & Buckner, “A Philosophical Introduction to Language Models — Part I: Continuity With Classic Debates,” arXiv:2401.03910, 2024
- Mirzadeh, Alizadeh, Shahrokhi, Tuzel, Bengio & Farajtabar, “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models,” ICLR 2025
- Mitchell & Krakauer, “The Debate Over Understanding in AI’s Large Language Models,” arXiv:2210.13966, 2023
- Mitiaeva & Xiao, “Talking past the machine: morality, politeness, and alignment in human–AI dialogue,” arXiv:2609.21401, 2026
- Narayanan & Kapoor, “AI as Normal Technology: An alternative to the vision of AI as a potential superintelligence,” Knight First Amendment Institute, 15 April 2025
- Shanahan, McDonell & Reynolds, “Role-Play with Large Language Models,” arXiv:2305.16367, 2023
- Shao, Zope, Jiang, Pei, Nguyen, Brynjolfsson & Yang, “Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce,” arXiv:2506.06576, 2026
- State Council of the People’s Republic of China, “Opinions on Deepening the Implementation of the ‘Artificial Intelligence+’ Initiative,” 26 August 2025 (English translation by CSET)
- van der Vlist, Helmond & Ferrari, “Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence,” Big Data & Society, 2024
- Wu et al., “Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks,” NAACL 2024
- Yiu, Kosoy & Gopnik, “Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?”, arXiv:2305.07666, 2023
LD-02: Aggregate null or distributional shift? Employment
- Aldasoro et al., BIS Working Paper 1325, January 2026
- Bick, Blandin & Deming Real-Time Population Survey / Federal Reserve FRED series, Q2 2026
- Bonney et al., “The Microstructure of AI Diffusion,” US Census CES-WP-26-25, April 2026
- Brynjolfsson, Chandar & Chen, “Canaries in the Coal Mine?”, Stanford Digital Economy Lab, revised 12 August 2026
- Brynjolfsson, Li & Raymond, “Generative AI at Work,” Quarterly Journal of Economics 140(2), 2025
- Chandar & Klein Teeselink, “How Does AI Change Labor Demand? Evidence from 41 Countries,” September 2026
- Cui et al., “The Effects of Generative AI on High-Skilled Work,” Management Science, 2026
- Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026
- Dell’Acqua et al., “Navigating the Jagged Technological Frontier,” Organization Science 37(2), 2026
- Hernæs & Kostøl, IZA Discussion Paper 18767, June 2026
- Hui, Reshef & Zhou, “The Short-Term Effects of Generative Artificial Intelligence on Employment,” Organization Science 35(6), 2024
- Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026
- Klein Teeselink, “Generative AI and Labor Market Outcomes: Evidence from the United Kingdom,” SSRN, revised December 2025
- Orr, Tucker & Warren, “Graduating into Disruption,” US Census CES-WP-26-56, September 2026
- R05 — HQ Cross-Batch Synthesis & Adjudication v0.1
- The Budget Lab at Yale, “Tracking the Impact of AI on the Labor Market,” updated 15 September 2026
- The Budget Lab at Yale, “What We Do and Don’t Know About How AI is Affecting the Labor Market,” 7 May 2026
- Tucker, “You’re (not) Hired,” US Census CES-WP-26-27, April 2026
LD-03: Organizational absorption: Why micro gains do not become macro outcomes
- Acemoglu, “The Simple Macroeconomics of AI,” Economic Policy 40(121), 2025
- Aldasoro et al., BIS Working Paper 1325, January 2026
- Baslandze, Edwards, Graham et al., “Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives,” NBER Working Paper 34984, March 2026
- Becker, “Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity,” METR, 11 May 2026
- Bick, Blandin & Deming Real-Time Population Survey / Federal Reserve FRED series, Q2 2026
- Bick, Blandin & Deming, “The Rapid Adoption of Generative AI,” Federal Reserve Bank of St. Louis Working Paper 2024-027F, October 2025
- Bick, Blandin, Deming & Schumacher, “What Work Does Generative AI Do?,” Federal Reserve Bank of St. Louis Working Paper 2026-017A, August 2026
- Bick, Blandin, Deming, Fuchs-Schündeln & Jessen, “Mind the Gap: AI Adoption in Europe and the US,” Federal Reserve Bank of St. Louis Working Paper 2026-003B, 2026
- Bonney et al., “The Microstructure of AI Diffusion,” US Census CES-WP-26-25, April 2026
- Brynjolfsson & Hitt, “Beyond Computation: Information Technology, Organizational Transformation and Business Performance,” Journal of Economic Perspectives 14(4), 2000
- Brynjolfsson, Li & Raymond, “Generative AI at Work,” Quarterly Journal of Economics 140(2), 2025
- Brynjolfsson, Rock & Syverson, “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” American Economic Journal: Macroeconomics 13(1), 2021
- Chen, Gascó-Hernandez & Esteve, “The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments,” American Review of Public Administration 54(3), 2024
- Cui et al., “The Effects of Generative AI on High-Skilled Work,” Management Science, 2026
- Cunningham & Whitfill, “Task Substitution and Uplift,” METR, 8 May 2026
- Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026
- Dell’Acqua et al., “Navigating the Jagged Technological Frontier,” Organization Science 37(2), 2026
- Dell’Acqua, Ayoubi, Lifshitz et al., “The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork,” Organization Science 37(4), 2026
- Dillon, Jaffe, Immorlica & Stanton, “Shifting Work Patterns with Generative AI,” NBER Working Paper 33795, revised November 2025
- Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026
- Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025
- He, Miller, Agarwal, Kästner & Vasilescu, “Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects,” Proceedings of MSR 2026
- Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026
- Klein Teeselink, “Generative AI and Labor Market Outcomes: Evidence from the United Kingdom,” SSRN, revised December 2025
- Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022
- Miller, Choudhuri, Ulloa et al., “‘Maybe We Need Some More Examples:’ Individual and Team Drivers of Developer GenAI Tool Use,” Proceedings of ICSE 2026
- Neumann, Bischof, Hinz et al., “Between Policy and Practice: GenAI Adoption in Agile Software Development Teams,” XP 2026, LNBIP
- R05 — HQ Cross-Batch Synthesis & Adjudication v0.1
- Rein, “Research Update: Algorithmic vs. Holistic Evaluation,” METR, 13 August 2025
- Tangi, Rodriguez Müller & Combetto, “A Silent Partner: The Shadow Presence of Generative Artificial Intelligence in Public Administrations,” Electronic Participation (ePart 2025), LNCS
- Xiao, Hu, Xiao, Cao & Shen, “Can GenAI Move from Individual Use to Collaborative Work?,” Proceedings of CHI 2026
- Yotzov, Barrero, Bloom et al., “Firm Data on AI,” NBER Working Paper 34836, revised March 2026
- Yu, Fleming, Hampton, Combemale & Thompson, “AI Adoption in S&P 500 Firms,” arXiv:2607.08920, July 2026
LD-04: Levelling or hollowing? Skill compression and the reproduction of expertise
- Autor, Rodchenko, Martin, Iscenko, Strand, Pearl & Ferere, “Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting,” NBER Working Paper 35720, 2026
- Bastani, Bastani, Sungu, Ge, Kabakcı & Mariman, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” PNAS 122(26), 2025
- Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019
- Becker, Rush, Barnes & Rein, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity,” arXiv:2507.09089, July 2025
- Brynjolfsson, Chandar & Chen, “Canaries in the Coal Mine?”, Stanford Digital Economy Lab, revised 12 August 2026
- Brynjolfsson, Li & Raymond, “Generative AI at Work,” arXiv:2304.11771 v2, November 2024
- Brynjolfsson, Li & Raymond, “Generative AI at Work,” Quarterly Journal of Economics 140(2), 2025
- Budzyń et al., “Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study,” Lancet Gastroenterology & Hepatology 10(10), 2025
- Cruces, Fernández Meijide, Galiani, Gálvez & Lombardi, “Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment,” arXiv:2608.04198, submitted 4 August 2026
- Cui et al., “The Effects of Generative AI on High-Skilled Work,” Management Science, 2026
- Davis, “AI is simultaneously aiding and replacing workers, wage data suggest,” Federal Reserve Bank of Dallas, 24 February 2026
- Dell’Acqua et al., “Navigating the Jagged Technological Frontier,” Organization Science 37(2), 2026
- Haslberger, Gingrich & Bhatia, “No Great Equalizer: Experimental Evidence on Productivity Effects of Generative AI Use in the UK Labor Market,” SSRN Working Paper 4594466, 2023
- Hernæs & Kostøl, IZA Discussion Paper 18767, June 2026
- Hosseini Maasoum & Lichtinger, “Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data,” SSRN Working Paper 5425555, version of 6 June 2026
- Huang, Jin & Li, “Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI’s Impact on Creative Tasks,” arXiv:2412.03963, December 2024
- Hui, Reshef & Zhou, “The Short-Term Effects of Generative Artificial Intelligence on Employment,” Organization Science 35(6), 2024
- Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026
- Idan & Anand, “Generative AI and the Productivity Divide: Human-AI Complementarities in Education,” arXiv:2605.18143, May 2026
- Ide & Talamàs, “Artificial Intelligence in the Knowledge Economy,” Journal of Political Economy, doi:10.1086/737233 (abstract read in arXiv:2312.05481 v12, May 2025)
- Ide, “Automation, AI, and the Intergenerational Transmission of Knowledge,” arXiv:2507.16078 v9, June 2026
- Lehmann, Cornelius & Sting, “AI Meets the Classroom: When Do Large Language Models Harm Learning?,” arXiv:2409.09047 v2, March 2025
- Lodefalk, Löthman, Koch & Engberg, “Same Storm, Different Boats: Generative AI and the Age Gradient in Hiring,” Örebro University Working Paper 2/2026, version of 16 March 2026
- Maier, Gunzenhäuser, Schweisthal, Schneider & Feuerriegel, “A meta-analysis of the effect of generative AI on productivity and learning in programming,” arXiv:2605.04779, May 2026
- METR, “We are Changing our Developer Productivity Experiment Design,” 24 February 2026
- Noy & Zhang, “Experimental evidence on the productivity effects of generative artificial intelligence,” Science 381(6654), 2023
- Otis, Clarke, Delecourt, Holtz & Koning, “The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya,” Management Science, 2026
- Peng, Kalliamvakou, Cihon & Demirer, “The Impact of AI on Developer Productivity: Evidence from GitHub Copilot,” arXiv:2302.06590, February 2023
- Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026
LD-05: Discretion curtailed, relocated or encoded?
- Albright, “If You Give a Judge a Risk Score: Evidence from Kentucky Bail Decisions,” Harvard John M. Olin Fellows’ Discussion Paper 85 (2019)
- Autoriteit Persoonsgegevens, childcare-benefits nationality-processing investigation/fine, 2021
- Bruff & Groves. “Scribe and prejudice?” Ada Lovelace Institute, 11 February 2026
- BVwG W256 2235360-1, decision 1 Sep 2025; prior VwGH/BVwG procedural history
- Cheng et al., “How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions,” CHI 2022
- CNAF/CAF, DMDE 2026 transparency and source-code documentation
- Cross-case synthesis of Poland; Allegheny; DWP; Austria; Ada Lovelace social-care evidence
- DUO official follow-up / Algorithm Audit materials, 2024–2026
- DWP, Effectiveness Assessment of Universal Credit Advances Model, 9 July 2026
- DWP, Universal Credit Advances Model — Fairness Assessment, 17 July 2025
- DWP; DUO; Toeslagen; Poland legal distinction
- DWP; Home Office; Ada Lovelace; Robodebt synthesis
- ICIBI 2023 inspection; Home Office transparency/guidance; JCWI/Foxglove litigation record for claimant position
- IMY, Försäkringskassan supervision decision, 2025
- National Audit Office, DWP fraud/error reporting, 2025
- Report of the Royal Commission into the Robodebt Scheme, 2023
- Stapleton et al., “Extended Analysis of ‘How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions’,” arXiv:2204.13872
- Stevenson, “Assessing Risk Assessment in Action,” Minnesota Law Review 103 (2018)
- Sztandar-Sztanderska & Zieleńska, “Changing social citizenship through information technology,” Social Work & Society 16(2), 2018
- UK Algorithmic Transparency Recording Standard record — DWP Universal Credit Advances model
LD-06: The evaluation apparatus: Can benchmarks, leaderboards and audits carry the weight placed on them?
- Bavaresco, Bernardi, Bertolazzi, Elliott, Fernández, Gatt et al., “LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks,” ACL 2025, arXiv:2406.18403
- Bean, Kearns, Romanou, Hafner, Mayne, Batzner et al., “Measuring what Matters: Construct Validity in Large Language Model Benchmarks,” NeurIPS 2025 Datasets and Benchmarks Track, arXiv:2511.04703
- Bengio, Clare, Prunkl, Andriushchenko, Bucknall, Murray et al., “International AI Safety Report 2026,” arXiv:2602.21012, February 2026
- Birhane, Steed, Ojewale, Vecchione & Raji, “AI auditing: The Broken Bus on the Road to AI Accountability,” IEEE SaTML 2024, arXiv:2401.14462
- Costanza-Chock, Harvey, Raji, Czernuszenko & Buolamwini, “Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem,” FAccT 2022, arXiv:2310.02521
- European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025
- Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897
- Gema, Leang, Hong et al., “Are We Done with MMLU?,” arXiv:2406.04127 (v3), January 2025
- Hardy, Reuel, Jafari Meimandi, Soder, Griffith, Asmar, Koyejo, Bernstein & Kochenderfer, “More than Marketing? On the Information Value of AI Benchmarks for Practitioners,” arXiv:2412.05520, December 2024
- LMArena, “Our Response to ‘The Leaderboard Illusion’ Writeup,” Arena blog, 9 May 2025 (updated 13 June 2025)
- Maia Polo, Weber, Choshen, Sun, Xu & Yurochkin, “tinyBenchmarks: evaluating LLMs with fewer examples,” ICML 2024, arXiv:2402.14992
- Needham, Edkins, Pimpale, Bartsch & Hobbhahn, “Large Language Models Often Know When They Are Being Evaluated,” arXiv:2505.23836 (v3), July 2025
- Reuel, Hardy, Smith, Lamparth, Hardy & Kochenderfer, “BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices,” NeurIPS 2024, arXiv:2411.12990
- Sajadieh, Fattorini, Perrault, Gil, Parli et al., “The AI Index 2026 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, April 2026
- Schoen, Nitishinskaya, Balesni, Højmark, Hofstätter, Scheurer et al., “Stress Testing Deliberative Alignment for Anti-Scheming Training,” arXiv:2509.15541, September 2025
- Sclar, Choi, Tsvetkov & Suhr, “Quantifying Language Models’ Sensitivity to Spurious Features in Prompt Design,” ICLR 2024, arXiv:2310.11324
- Singh, Nan, Wang, D’Souza, Kapoor, Üstün, Koyejo, Deng, Longpre, Smith, Ermis, Fadaee & Hooker, “The Leaderboard Illusion,” arXiv:2504.20879 (v2), May 2025
- Singh, Romanou, Fourrier, Adelani, Ngui, Vila-Suero et al., “Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation,” arXiv:2412.03304 (v2), 2025
- State Administration for Market Regulation & Standardization Administration of China, GB/T 45654-2025, “Cybersecurity Technology — Basic Safety Requirements for Generative Artificial Intelligence Services,” issued 25 April 2025 (CSET English translation, 28 May 2026)
- Taylor, Black, Bowen, Read, Golechha et al., “Auditing Games for Sandbagging,” arXiv:2512.07810, December 2025
- Truong, Tu, Hardy, Reuel, Tang, Burapacheep, Perera, Uwakwe, Domingue, Haber & Koyejo, “Fantastic Bugs and Where to Find Them in AI Benchmarks,” arXiv:2511.16842, November 2025
- US AI Safety Institute & UK AI Safety Institute, “US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1,” December 2024
- Vendrow, Vendrow, Beery & Madry, “Do Large Language Model Benchmarks Test Reliability?,” arXiv:2502.03461, February 2025
- Wan, Klyman, Kapoor, Maslej, Longpre, Xiong, Liang & Bommasani, “The 2025 Foundation Model Transparency Index,” arXiv:2512.10169, December 2025
- Whitfill, Wu, Becker & Rush, “Many SWE-bench-Passing PRs Would Not Be Merged into Main,” METR research note, 10 March 2026
- Wolfrath, Conroy, Kosten, Bell, Neupane, Kindel et al., “Model Retirement Creates Reproducibility Risk in Biomedical AI Publications,” arXiv:2609.04699, September 2026
- Zhang, Da, Lee, Robinson, Wu, Song et al., “A Careful Examination of Large Language Model Performance on Grade School Arithmetic,” NeurIPS 2024 Datasets and Benchmarks Track, arXiv:2405.00332
- Zheng, Chiang, Sheng et al., “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena,” NeurIPS 2023 Datasets and Benchmarks Track, arXiv:2306.05685
- Zhu, Jin, Pruksachatkun, Zhang, Liu, Cui et al., “Establishing Best Practices for Building Rigorous Agentic Benchmarks,” arXiv:2507.02825 (v5), 2025
LD-07: AI in the research apparatus
- “Large language models for thematic analysis in healthcare research: A blinded mixed-methods comparison with human analysts,” PLOS Digital Health 2026
- Argyle, Busby, Fulda, Gubler, Rytting & Wingate, “Out of One, Many: Using Language Models to Simulate Human Samples,” arXiv:2209.06899 v1, September 2022
- Asher, Gold, Chen & Carvalho, “Chatbots Are Undermining Crowdsourced Research in the Behavioral Sciences,” AMPPS 9(1) (2026)
- Ashokkumar, Hewitt, Ghezae & Willer, “Large language models can predict the results of social science experiments,” Nature 656:115–122 (2026)
- Baumann, Röttger, Urman, Wendsjö, Plaza-del-Arco, Gruber & Hovy, “Large Language Model Hacking,” arXiv:2509.08825v2
- Bisbee, Clinton, Dorff, Kenkel & Larson, “Synthetic Replacements for Human Survey Data? The Perils of Large Language Models,” Political Analysis 32(4):401–416 (2024)
- Calderon, Reichart & Dror, “The Alternative Annotator Test for LLM-as-a-Judge,” ACL 2025
- Chen, Urminsky et al., “Estimating the threat of AI-agent responding across online survey platforms,” PsyArXiv 2026
- Chen, Zaharia & Zou, “How is ChatGPT’s behavior changing over time?,” Harvard Data Science Review 6(2) (2024)
- Egami, Hinck, Stewart & Wei, “Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models,” NeurIPS 2023
- Gong Weigang & Huang Siyuan, “整体事实还是有偏样本——基于大语言模型生成数据的测量,” Academic Monthly 57(6):123–136 (2025)
- Gordon et al., “AI Agent Prevalence and Data Quality Across Multiple Online Sample Providers,” PsyArXiv 2026
- Li & Ji, “Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments,” arXiv:2604.02458
- Lin, “Artificial-Intelligence-Mediated Contamination in Online Research: Taxonomy, Risk Gradient, and Recommendations,” AMPPS 9(3) (2026)
- Ling Wanying & Cui Sizhan, “复现的限度——‘硅基样本’的可能与可为,” Journal of Intelligent Society 4(2):179–201 (2025)
- Liu et al., “Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth,” arXiv:2607.28890
- Nakamura, Tan & Yean, “Observational Equivalence of LLM and Human Annotation,” arXiv:2609.22133
- Pan & Xu, “Political censorship in large language models originating from China,” PNAS Nexus 5(2):pgag013 (2026)
- Panickssery, Bowman & Feng, “LLM Evaluators Recognize and Favor Their Own Generations,” NeurIPS 2024
- Shi et al., “Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge,” IJCNLP-AACL 2025
- Than, Fan, Law, Nelson & McCall, “Updating ‘The Future of Coding’: Qualitative Coding with Generative Large Language Models,” Sociological Methods & Research 54(3):849–888 (2025)
- Veselovsky, Horta Ribeiro, Cozzolino, Gordon, Rothschild & West, “Prevalence and prevention of large language model use in crowd work,” CACM 2025
- Wang et al. NMI 2025
- Westwood & Frederick, “Reply to Van der Stigchel et al.: Empirical evidence that AI survey contamination is real and substantial,” PNAS 123(8):e2537420123 (2026)
- Westwood, “The potential existential threat of large language models to online survey research,” PNAS 122(47):e2518075122 (2025)
- Xie et al., “Evaluating the statistical realism of LLM-generated social science data,” PNAS 123(19):e2538145123 (2026)
- Xu & Malkin, “A Penny for Your Prompts: Experiments Detecting and Mitigating LLM Usage by Survey Respondents,” arXiv:2607.00403
- Yang, Wang, Zhou & Xu, “Data Annotation with Large Language Models: Lessons from a Large Empirical Evaluation,” conditionally accepted at AJPS; pre-publication manuscript
- Zhang, Xu & Alvero, “Generative AI Meets Open-Ended Survey Responses,” Sociological Methods & Research 54(3):1197–1242 (2025)
LD-08: Who can know about AI? Epistemic dependence and jurisdiction over the field
- Abdalla & Abdalla, “The Grey Hoodie Project: Big Tobacco, Big Tech, and the Threat on Academic Integrity,” AIES 2021
- Abdalla, Wahle, Ruas, Névéol, Ducel, Mohammad & Fort, “The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research,” ACL 2023
- Ahmed & Wahed, “The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research,” arXiv:2010.15581, 2020
- Ahmed, Das, Martin & Banerjee, “The Narrow Depth and Breadth of Corporate Responsible AI Research,” arXiv:2405.12193, 2026
- Aitken, Abdalla, Rudie & Stinson, “Collaboration or Corporate Capture? Quantifying NLP’s Reliance on Industry Artifacts and Contributions,” ACL 2024
- Anthropic, “A research agenda for the Economic Futures Research Fund,” 22 July 2026, as served on 16 October 2026
- Anthropic, “Anthropic Economic Futures Program,” programme page, as served on 16 October 2026
- Anthropic, “Anthropic Economic Index report: Economic primitives,” 15 January 2026
- Anthropic, “Anthropic Economic Index report: Uneven geographic and enterprise AI adoption,” 15 September 2025
- Anthropic, “External Researcher Access Program,” Claude help centre page, as served on 16 October 2026
- Anthropic, “Introducing the Anthropic Economic Advisory Council,” 28 April 2025 (updated 9 May 2025), as served on 16 October 2026
- Bak-Coleman, O’Connor, Bergstrom & West, “The Risks of Industry Influence in Tech Research,” arXiv:2510.19894, 2025
- Besiroglu, Bergerson, Michael, Heim, Luo & Thompson, “The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?”, arXiv:2401.02452, 2024
- Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022
- Brynjolfsson, Chandar & Chen, “Canaries in the Coal Mine?”, Stanford Digital Economy Lab, revised 12 August 2026
- Chatterji, Cunningham, Deming, Hitzig, Ong, Shan & Wadman, “How People Use ChatGPT,” NBER Working Paper 34255, 2025
- European Commission, “Commission accepts X’s action plan to comply with Digital Services Act,” 16 July 2026, as served on 16 October 2026
- European Commission, “Commission adopts delegated act on data access under the Digital Services Act,” 2 July 2025, as served on 16 October 2026
- European Commission, “Commission fines X €120 million under the Digital Services Act,” press release IP/25/2934, 5 December 2025
- European Commission, “Commission holds roundtable on data access for vetted researchers,” 20 May 2026, as served on 16 October 2026
- Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025
- Green, “The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice,” Journal of Social Computing, 2021
- Hagendorff & Meding, “Ethical considerations and statistical analysis of industry involvement in machine learning research,” AI & Society, 2021
- Handa, Tamkin, McCain, Huang, Durmus et al., “Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations,” arXiv:2503.04761, 2025
- Humlum & Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026
- Jobin, Ienca & Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, 2019
- Jurowetzki, Hain, Mateos-Garcia & Stathoulopoulos, “The Privatization of AI Research(-ers): Causes and Potential Consequences,” arXiv:2102.01648, 2021
- Klinger, Mateos-Garcia & Stathoulopoulos, “A narrowing of AI research?”, arXiv:2009.10385, 2022
- Laufer, Jain, Cooper, Kleinberg & Heidari, “Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects,” FAccT 2022
- Longpre, Kapoor, Klyman, Ramaswami, Bommasani et al., “A Safe Harbor for AI Evaluation and Red Teaming,” arXiv:2403.04893, 2024
- Ma, Gero, Canonne, Jin & Thilakarathna, “How People Use ChatGPT in Australia: A WildChat Analysis,” arXiv:2609.28990, 2026
- Markus, Gilardi & Stetsenko, “Societal AI Research Has Become Less Interdisciplinary,” arXiv:2506.08738, 2025
- Microsoft Research, “AI Economy Institute (AIEI) Program,” call for proposals, 2025, as served on 16 October 2026
- Murtfeldt, Paik, Alterman, Kahveci & West, “RIP Twitter API: A Eulogy to Its Vast Research Contributions,” arXiv:2404.07340, 2025
- Roy Chowdhury & Garimella, “How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries,” arXiv:2610.18465, 2026
- Tamkin, McCain, Handa, Durmus et al., “Clio: Privacy-Preserving Insights into Real-World AI Use,” arXiv:2412.13678, 2024
- Tomlinson, Jaffe, Wang, Counts & Suri, “Working with AI: Measuring the Applicability of Generative AI to Occupations,” arXiv:2507.07935, 2025
LD-09: Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike?
- Agarwal, Naaman & Vashistha, “AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances,” CHI 2025, arXiv:2409.11360
- Anderson, Shah & Kreminski, “Homogenization Effects of Large Language Models on Human Creative Ideation,” ACM Creativity & Cognition 2024, arXiv:2402.01536
- Ashkinaze, Mendelsohn, Li Qiwei, Budak & Gilbert, “How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment,” ACM Collective Intelligence 2025, arXiv:2401.13481
- Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371
- Burtch, “The Hitchhiker’s Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semantics,” arXiv:2607.13077, 2026
- Burtch, Lee & Chen, “The consequences of generative AI for online knowledge communities,” Scientific Reports 14, 10413, 2024
- del Rio-Chanona, Laurentsyeva & Wachs, “Large language models reduce public knowledge sharing on online Q&A platforms,” PNAS Nexus 3, pgae400, 2024
- Doshi & Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances 10, eadn5290, 2024
- Evans, “Electronic Publication and the Narrowing of Science and Scholarship,” Science 321, 2008
- Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)
- Gerstgrasser, Schaeffer, Dey, Rafailov, Sleight, Hughes et al., “Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data,” arXiv:2404.01413, 2024
- Hackenburg et al., Science 2025 / arXiv full text
- Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026
- Hedden & Raghavan, “Algorithmic monoculture and its critics,” Philosophical Perspectives, 2026 (arXiv:2604.06047)
- Jo, Garg & Raghavan, “The Subjectivity of Monoculture,” arXiv:2602.24086, 2026
- Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962
- Kleinberg & Raghavan, “Algorithmic monoculture and social welfare,” PNAS 118, 2021 (arXiv:2101.05853)
- Kumar, Vincentius, Jordan & Anderson, “Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking,” CHI 2025, arXiv:2410.03703
- Kusumegi et al., “A robust association between LLM use and scientific productivity: Assessing stopping-time selection,” arXiv:2607.28968, July 2026
- Kusumegi, Yang, Ginsparg, de Vaan, Stuart & Yin, “Scientific production in the era of large language models,” Science 390, 2025 (author version arXiv:2601.13187)
- Liang, Zhang, Wu, Lepp, Ji, Zhao et al., “Mapping the Increasing Use of LLMs in Scientific Papers,” arXiv:2404.01268, April 2024
- Meincke, Nave & Terwiesch, “ChatGPT Decreases Idea Diversity in Brainstorming,” author manuscript, The Wharton School, University of Pennsylvania
- Messeri & Crockett, “Artificial intelligence and illusions of understanding in scientific research,” Nature 627, 2024
- Padmakumar & He, “Does Writing with Language Models Reduce Content Diversity?,” ICLR 2024, arXiv:2309.05196
- Renault, Bergeaud & Bosquet, “Comment on Scientific production in the era of large language models,” arXiv:2605.17979, May 2026
- Shumailov, Shumaylov, Zhao, Papernot, Anderson & Gal, “AI models collapse when trained on recursively generated data,” Nature 631, 2024
- Si, Hashimoto & Yang, “The Ideation–Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas,” arXiv:2506.20803, June 2025
- Si, Yang & Hashimoto, “Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers,” ICLR 2025, arXiv:2409.04109
- Terwiesch, Meincke, Girotra, Mollick, Nave & Ulrich, “AI and Its Impact on Creativity and Diversity: An Empirical Study of LLM-Generated Product Ideas,” arXiv:2607.27553, 2026
- Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862
- Wright, Masud, Moore et al., “What and whose knowledge? Measuring epistemic diversity in large language models,” arXiv:2510.04226, accepted to EMNLP 2026
- Xue, Wang, Zheng, Li & Tan, “Can ChatGPT Kill User-Generated Q&A Platforms?,” Information Systems Research, 2026
- Zheng, Hong, Liu & Ni, “Scientific exploration, collaboration and labor division in the large language model era,” arXiv:2607.20923, July 2026
- Zhou & Lee, “Generative artificial intelligence, human creativity, and art,” PNAS Nexus 3, pgae052, 2024
LD-10: Persuasion, Intermediation & the Information Order
- AI Act Art. 50
- CETaS / Alan Turing Institute, Sep 2024
- Chen et al., Journal of Experimental Political Science 2026
- Costello et al., Science, 2024 — conspiracy-belief experiment
- Dufour et al., AMMeBa, arXiv:2405.11697
- EBU/BBC, News Integrity in AI Assistants, Oct 2025
- European Commission, final Transparency Code, 10 Jun 2026
- Gallegos et al., PNAS Nexus 2026
- Gholami, Firullo, Cheyre & Acquisti 2026, SSRN 6238578
- Hackenburg et al. 2026, arXiv:2604.09200
- Hackenburg et al. 2026, arXiv:2606.16475
- Hackenburg et al., Science 2025 / arXiv full text
- Hölbling, Maier & Feuerriegel, Scientific Reports 2025
- Li & Aral 2025, arXiv:2504.06435
- Lin et al., Nature 2025 + Supplementary Information
- Pan & Xu, “Political censorship in large language models originating from China,” PNAS Nexus 5(2):pgag013 (2026)
- Pew Research Center, 22 Jul 2025
- PNAS Nexus 2025 misleading-image labelling study
- R07 — HQ Cross-Batch Synthesis & Adjudication v0.1
- Reuters Institute Digital News Report 2026
- Reuters Institute DNR 2026 chapter + executive summary
- Saharsh Agarwal & Ananya Sen, “The Impact of Google AI Overviews on Publisher Traffic and User Experience: Evidence from a Field Experiment,” SSRN 6513059.
- Salvi et al. Author Correction, 3 September 2026
- Salvi et al., Nature Human Behaviour, 2025 — personalized GPT-4 persuasion experiment
- Science Editorial Expression of Concern, 11 June 2026
- Wang et al. 2026, arXiv:2608.18352
- Xu, Iqbal & Montgomery 2026, arXiv:2605.14021
LD-11: Concentration, Stack & Sovereignty
- AI Gigafactories call, 30 Jul 2026
- Anthropic 2026 compute announcements
- ASML 2025 Annual Report
- Broadcom SEC 8-K
- CMA package of actions, 31 March 2026
- Counterpoint Research, Global DRAM and HBM Market Share, Sep 2026
- Epoch AI, 7 Apr 2026
- Epoch AI, Emberson, You & Somala, 14 Apr 2026
- Epoch AI, The Plunging Price of Thought, Sep 2026
- European Commission CADA proposal, 3 Jun 2026
- European Commission, 25 Jun 2026 preliminary DMA position
- FTC Office of Technology 6(b) staff report, Jan 2025
- Menlo Ventures, 2025 State of Generative AI in the Enterprise
- Microsoft/OpenAI joint announcements, 28 Oct 2025 and 27 Apr 2026
- Mubadala 2026 Letter to Partners
- NSF NAIRR pages and 1 Sep 2026 Operations Center announcement
- NVIDIA Form 10-Q, quarter ended 26 Jul 2026
- NVIDIA Q2 FY2027 reporting and SEC filing
- OpenRouter/a16z, State of AI: 100 Trillion Token Study
- R06 — HQ Cross-Batch Synthesis & Adjudication v0.1
- R06 HQ verification
- Synergy Research Group, 30 Jul 2026
- TrendForce, 2Q26 foundry release, 9 Sep 2026
- U.S. Department of Commerce, 19 Nov 2025
- UK CMA Cloud Services Market Investigation, final decision 31 Jul 2025
LD-12: Regulation-in-Practice: Rights, Compliance & Developmental Steering
- Agence Europe relay of Commission statement
- CAC generative-AI filing notices
- CEN-CENELEC EN 18286 publication
- CJEU C-203/22, ECLI:EU:C:2025:117
- Colorado General Assembly SB24-205, SB25B-004, SB26-189
- Court of Rome R.G. 4785/2025 judgment
- DOJ AI Litigation Task Force memorandum
- EuroHPC JU AIGF call
- European Commission AI Act standardisation guidance
- European Commission AI Gigafactories page
- European Commission enforcement framework
- European Commission GPAI Code and provider guidelines
- European Commission, Market Surveillance Authorities under the AI Act
- Executive Order 14365
- Garante Decision No. 755 of 2 Nov 2024
- IAPP/Credo AI, AI Governance Profession Report 2025
- Korean National Law Information Center
- NYS Comptroller Audit 2024-N-6
- official fiscal/court-order record
- R08 HQ Batches 01–03 + canonical claim set
- Regulation (EU) 2026/1744; European Commission AI Omnibus implementation material
- South Korean Ministry of Science and ICT official Sovereign AI Foundation Model programme releases
- U.S. ITA implementation report
- Wright et al., “Null Compliance,” FAccT 2024
- 国务院关于深入实施“人工智能+”行动的意见
Not cited by a current debate
- Demirer, Fradkin, Tadelis & Peng, “The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs,” NBER Working Paper 34608, December 2025
- Geoffrey C. Bowker & Susan Leigh Star, Sorting Things Out: Classification and Its Consequences, 1999
- Ian Hacking, work on kinds of people / moving targets / looping effects
- John Hardwig, “Epistemic Dependence,” The Journal of Philosophy 82(7), 1985
- Julia Pohle & Thorsten Thiel, “Digital sovereignty,” Internet Policy Review 9(4), 2020
- Michael Lipsky, Street-Level Bureaucracy: Dilemmas of the Individual in Public Services
- Michel Foucault, Security, Territory, Population — Collège de France lectures 1977–1978
- Michel Foucault, The Birth of Biopolitics — Collège de France lectures 1978–1979
- Paul Feyerabend, Against Method
- Regulation (EU) 2026/1744 (Digital Omnibus on AI), 8 Jul 2026