Research opportunities

Research directions that follow from what the evidence in a debate does not yet settle. Each one links to the claims that motivate it, sketches a feasible design, and records the literature screening done before it was published.

These are hypotheses about where useful work lies, drafted with AI assistance and screened by AI web search. Those marked first-pass screen only have not yet been searched again in more depth; the others have. No person or domain expert has checked the searches yet, and any search can miss relevant work, so check the nearest studies listed on each page before building a project on one.

Living Debate LD-02

Why do Nordic registers and US data disagree about young workers in AI-exposed jobs?One PhD chapter or paperLow costPartly addressedUntested beyond the original settingWhen the same exposure measure, age bands, occupation coding and event-study specification are applied to US data and to Nordic population registers, how much of the gap between the large US young-worker divergence and the near-zero Nordic estimates is explained by occupation mix, education composition, sensitivity to interest-rate rises and cohort size, and how much remains?
Does the young-worker hiring gap hold up with survey-measured AI use instead of exposure?Master's thesisLow costPartly addressedWrong or missing validation targetIf occupation-level AI exposure indices are replaced by AI-use rates measured in a nationally representative worker survey, does the relative fall in hiring of 22–25-year-olds into exposed occupations in US Current Population Survey data keep its size and sign, and how does the answer compare with exposure indices and chatbot-log usage measures?
Do AI-exposed graduates' early penalties fade or persist beyond the first two years?A PhD projectLow costPartly addressedNot yet measuredFor graduates who entered the labour market between 2022 and 2025, do the employment, earnings and occupational-match penalties of AI-exposed fields of study persist, fade or widen from the second to the fifth year after graduation, do graduates catch up by moving into exposed occupations later, and how do the size and decay of the penalty compare, in the same data and model, with the scarring of graduates who entered during an earlier recession?
Are juniors who still get hired at AI-adopting firms promoted and trained less?One PhD chapter or paperLow to medium costPartly addressedCausal mechanism not isolatedAmong workers hired into junior positions before and after their employer began using AI, do promotion into higher-skill occupations, wage growth, recorded training and retention over the following two to four years differ between AI-adopting and comparable non-adopting firms?
How much of a firm's AI productivity gain reaches the wages of workers who stay?One PhD chapter or paperLow to medium costPartly addressedCausal mechanism not isolatedWhen a firm starts using generative AI, how much does its value added per worker change over the following two to three years, and what share of that change shows up in the wages of incumbent workers who stay, compared with profits and with the wages of new hires?
Do French firms that sign AI agreements then hire and train differently?One PhD chapter or paperLow to medium costPartly addressedInstitutional practice unknownIn French firms, does signing a company-level agreement that addresses AI change subsequent hiring (junior versus experienced, AI-related versus other roles) and training compared with similar non-signing firms in the same sector, and do effects differ by type of commitment?

Living Debate LD-03

Does deploying AI to whole teams change the work that individual licences did not?Funded team projectNeeds a budget (medium or higher)Partly addressedCausal mechanism not isolatedFirst-pass screen onlyIn real organizations, when generative AI is deployed to whole teams or units, with and without a redesign of the team's workflow, do coordination-dependent work (meetings, handoffs, jointly written documents) and the unit's measured output change, compared with units where only some individuals have the tool?
Where does the time saved with generative AI go?A PhD projectLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyWhen workers save time with generative AI, how much of it goes to more of the same output, to new tasks, to checking AI output, to on-the-job leisure or to shorter hours, measured with time-use diaries or telemetry linked to the same workers' measured output?
How much of the time AI saves its users do colleagues spend checking and fixing?One PhD chapter or paperLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyIn organizations where some staff produce work with generative AI, how much time do reviewers, managers and recipients spend checking, correcting and redoing that work, and once both are counted, does the downstream cost offset the time the author saved?
Is there a generative-AI productivity J-curve inside firms?A PhD projectLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyIn a multi-year panel of firms, does deeper integration of generative AI, together with measured complementary investment (process redesign, training, data and capitalized software), show an early dip and a later rise in measured productivity, and do firms that adopt without that investment show neither?
Do generative-AI deployments in government improve services, not just staff time?Funded team projectNeeds a budget (medium or higher)Partly addressedWrong or missing validation targetFirst-pass screen onlyWhen a government agency deploys generative AI to caseworkers, adjudicators or reviewers, do service outcomes recorded in administrative data (processing time, error and appeal rates, outcomes for citizens) improve against a credible comparison group, beyond staff time savings?
Do organizations' AI policies match what their staff actually do with AI?A PhD projectLow costNo study foundInstitutional practice unknownFirst-pass screen onlyDo organizations' formal generative-AI policies, mandates and announcements match measured use and work outcomes, and where they diverge, is the divergence ceremonial adoption (policy without practice) or shadow use (practice without policy)?

Living Debate LD-04

Do professionals trained with AI lose the skill without it a year later?A PhD projectLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyAmong people learning a professional or technical skill (clinical reasoning, programming, legal drafting), are those randomly assigned to learn with generative AI less able to do the work without it 6 to 12 months later than comparable learners trained without it, measured against an unassisted baseline test?
Does retained skill depend on how routine the task is and who is learning?One PhD chapter or paperLow to medium costNo study foundCausal mechanism not isolatedFirst-pass screen onlyAfter several weeks of AI-assisted practice, does performance without the tool depend jointly on how routine the task is and on the worker's prior expertise, as the split between existing tool-removed studies suggests?
Do newcomers get less feedback and real work once AI coding tools arrive?One PhD chapter or paperLow costPartly addressedNot yet measuredFirst-pass screen onlyIn open-source projects, after AI coding tools are adopted, do first-time contributors receive less substantive review feedback, less mentoring and fewer tasks handed to them by experienced maintainers than in comparable projects that have not adopted them?
What AI set-ups do employers actually give trainees and junior staff?Master's thesisLow costNo study foundInstitutional practice unknownFirst-pass screen onlyIn law firms, hospitals and residency programmes, and software firms, what generative-AI tools, settings and rules do trainees and junior staff actually get, do they differ from what seniors get, and do any employers use the learning protections (hints instead of answers, AI-off practice) that experiments show protect skill?
Does skill compression reverse when AI acts as an agent rather than an assistant?One PhD chapter or paperLow to medium costPartly addressedUntested beyond the original settingFirst-pass screen onlyOn the same tasks, does the advantage that AI gives less skilled workers when it assists them (a copilot) shrink or reverse when it carries out the work autonomously (an agent), as theory predicts?
Do experienced clinicians lose unaided skill after routine AI use?Funded team projectNeeds a budget (medium or higher)Partly addressedCausal mechanism not isolatedFirst-pass screen onlyDoes routine use of diagnostic AI lower experienced clinicians' detection rates when they work without it, tested with randomized AI-off sessions scheduled over time and analysed by each clinician's cumulative AI exposure?

Living Debate LD-05

Outside corrections, how often do officials depart from algorithmic recommendations?Master's thesisLow costPartly addressedNot yet measuredAcross deployed public-sector decision-support systems in benefits, employment services, child welfare, pretrial and immigration, how often do officials formally or informally depart from the system's recommendation, in which direction, how much of the variation between systems survives once departure is counted the same way, and how do these rates compare with the established override literature for offender risk instruments?
Does telling reviewers a case was picked by a risk model change what they decide?One PhD chapter or paperLow to medium costPartly addressedCausal mechanism not isolatedWhen public officials review a case selected for extra scrutiny, does knowing that a risk model selected it, or seeing its risk score, change how often they decline the claim, how hard they look, and whether they catch errors planted upstream in the case file, and do these effects differ by the claimant's nationality?
What does being flagged by a fraud-risk model cost claimants before any decision?One PhD chapter or paperLow costPartly addressedNot yet measuredFor benefit claimants selected for extra checks by a risk model, how large are the burdens imposed before any formal decision (payment delay, evidence requests, withdrawn or abandoned claims, repeat applications), how do they compare with claimants selected at random or not selected, and how are they distributed by nationality and age?
How many AI transcription errors survive social workers' checks into case records?A PhD projectNeeds a budget (medium or higher)Partly addressedNot yet measuredIn local authorities using generative-AI transcription and summarisation in social work, what share of final, signed case records contain errors (invented content, omissions, changed meaning) that are absent from the recorded conversation, which kinds survive the practitioner's check, and what happens when people who draw on care read and try to correct them?
In contracts for public decision systems, who controls thresholds, variables and updates?One PhD chapter or paperLow costPartly addressedInstitutional practice unknownIn contracts and tender documents for public-sector risk-scoring, profiling and case-routing systems in several countries, who holds authority over thresholds, input variables, retraining and updates, what audit and documentation rights does the agency keep, and how does this differ between vendor-built and in-house systems?
Who first detects failures in public-sector algorithmic systems, and how late?One PhD chapter or paperLow costPartly addressedNot yet measuredFor documented failures of public-sector algorithmic systems since 2020, who first detected the problem (internal monitoring, frontline staff, auditors, regulators, courts, journalists, advocacy groups, affected people), how long after deployment, and did detection lead to one-case correction or to system-level redesign?

Living Debate LD-06

Do benchmark scores predict what models achieve in real deployments?A PhD projectLow to medium costPartly addressedWrong or missing validation targetFirst-pass screen onlyAcross many language models, do scores on public benchmarks predict measured outcomes when the same models are used in real work, such as code that maintainers merge in live repositories, support tickets resolved or error rates in use, and are the benchmarks calibrated to those outcomes or only correct in rank?
How faithfully do indices and policy documents relay AI evaluation results?Master's thesisLow costNo study foundInstitutional practice unknownFirst-pass screen onlyWhen annual indices, industry reports and government or procurement documents cite benchmark and evaluation figures, what share come from the developers themselves, what share are independently measured, and how often is the figure, its unit or its scope changed between the primary source and the relay?
How much does knowing it is tested change a model's measured safety?One PhD chapter or paperLow to medium costPartly addressedInstrument stabilityFirst-pass screen onlyWhen the same safety scenarios are presented with and without minimal cues that they are tests, how large is the gap in measured behaviour across open and closed frontier models, and does suppressing evaluation awareness in open-weight models on the same items reproduce the gap?
How is external evaluation under the EU AI Code of Practice carried out?A PhD projectLow costNo study foundInstitutional practice unknownFirst-pass screen onlyUnder the EU Code of Practice for general-purpose AI models with systemic risk, which signatories appoint independent external evaluators, how often are the exceptions used (a model judged similarly safe or safer, or no evaluator found after a public call), and what access and time do evaluators actually receive?
Who builds and checks China's compliance test banks for generative AI?A PhD projectLow to medium costNo study foundGeographic or language coverageFirst-pass screen onlyUnder China's national standard GB/T 45654-2025, who builds the generated-content and refusal test banks that decide whether a generative AI service passes, how are the banks validated, and do providers' banks and third-party evaluators' banks give the same verdicts on the same models?
How many published AI evaluations outside medicine can no longer be repeated?Master's thesisLow costPartly addressedUntested beyond the original settingFirst-pass screen onlyAmong published studies in NLP, the social sciences and education research that evaluate closed language models through an API, what share used a model version that has since been retired or is due to retire, and how many of a sample can still be re-run to reproduce their main result?

Living Debate LD-07

Do pinned model snapshots keep social-science conclusions stable over time?One PhD chapter or paperLow costPartly addressedInstrument stabilityOn a fixed battery of published social-science annotation tasks, how often does a substantive conclusion (sign, significance, interval coverage) change between repeated queries of a pinned model snapshot, a moving model alias over 6–12 months, and successive open-weight releases, and can design-based correction absorb those changes?
Does quantization or the serving engine change social-science findings?Master's thesisLow costPartly addressedInstrument stabilityWith weights, prompts and hardware held fixed, do fp16, 8-bit and 4-bit builds of open-weight models, and different inference engines, change substantive estimates on published annotation and synthetic-respondent tasks more than prompt paraphrasing does?
How much AI-assisted or agent responding reaches probability panels?One PhD chapter or paperLow to medium costNo study foundNot yet measuredIn probability-based panels, what share of completed interviews show AI assistance or autonomous-agent completion when measured with embedded multi-method detection validated against consented chatbot-history linkage, and how does that compare with opt-in platforms measured the same way?
Can synthetic respondents recover strong treatment-effect heterogeneity?One PhD chapter or paperLow costPartly addressedWrong or missing validation targetIn probability-sample survey experiments fielded after model training cutoffs and selected for substantial, well-powered moderation, do synthetic samples recover the sign and ranking of conditional treatment effects better than a model that assumes no heterogeneity?
Does post-training change what an LLM measures when it codes text?One PhD chapter or paperLow costPartly addressedCausal mechanism not isolatedHolding base weights fixed, how much do instruction tuning, preference tuning and region-specific fine-tunes shift coding validity and downstream estimates on stance and attitude annotation tasks, and do the shifts track the fine-tune's target culture?
Do AI literature-review tools change the conclusions of social-science reviews?One PhD chapter or paperLow costPartly addressedNot yet measuredAcross a sample of published social-science meta-analyses, do the studies retrieved by AI review tools, compared with those the original review included, change the pooled effect, its heterogeneity or the stated conclusion, and is the missed evidence systematically different (grey literature, non-English studies, null results)?
How do research ethics boards judge synthetic participants and AI-interpreted data?One PhD chapter or paperLow costPartly addressedInstitutional practice unknownHow do ethics-board guidance documents across countries classify synthetic participants, LLM coding or interpretation of qualitative data, and transfer of participant data to commercial model APIs, and in a factorial vignette, do board members' decisions and requested safeguards vary with data sensitivity, model hosting and the degree of delegated interpretation?
Do silicon samples work outside the North Atlantic, and in which language?A PhD projectLow to medium costPartly addressedGeographic or language coverageUsing Afrobarometer, Arab Barometer and Latinobarómetro items fielded after model training cutoffs, does synthetic-sample accuracy at country, subgroup and individual level depend on interview language (for example Swahili or Hausa versus English, Arabic versus English), and do LLM codes of political constructs in low-resource languages pass a replacement test against trained in-country coders?
Do LLM judges inflate the quality of AI-written deliberation?One PhD chapter or paperLow to medium costPartly addressedWrong or missing validation targetOn a corpus mixing human-written and LLM-written deliberative contributions, do LLM judges assign higher construct scores (for example on the Discourse Quality Index or argument quality) to LLM-authored text than expert coders blind to authorship, and is the gap larger when the judge model also wrote the text?
Does "LLMs can replace coders" depend on which humans you compare with?Master's thesisLow costPartly addressedWrong or missing validation targetHolding items and codebook fixed, how often does a formal replacement-test verdict flip depending on whether the comparator pool is crowdworkers, trained research assistants, the original study's coders or domain experts, and does the flip rate track construct ambiguity?

Living Debate LD-08

Does industry funding or data access change what research on AI's social effects finds?A PhD projectNeeds a budget (medium or higher)No study foundCausal mechanism not isolatedFirst-pass screen onlyAmong published studies of AI's social effects (labour, information, bias, education), do studies funded by AI developers, or relying on their data or access, reach systematically different conclusions from comparable independent studies, after accounting for topic, design and venue?
How tied to industry are researchers of AI's social effects outside computer science?One PhD chapter or paperLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyWhat share of authors of research on AI's social effects in social-science journals, interdisciplinary venues (FAccT, AIES) and policy reports have industry funding, employment or data agreements, how often is this disclosed, and how does it differ by discipline and region?
Do developers' usage statistics hold up when someone else classifies the conversations?One PhD chapter or paperNeeds a budget (medium or higher)Partly addressedInstrument stabilityFirst-pass screen onlyWhen the same chatbot conversations are classified independently of the developer, with a validated scheme and human-coded benchmarks, how far do headline usage measures such as the automation share, the work share and task success move away from the figures developers publish?
Does the EU's researcher-access law actually reach AI features, and who gets in?One PhD chapter or paperLow to medium costPartly addressedInstitutional practice unknownFirst-pass screen onlyUnder Article 40 of the Digital Services Act, which vetted-researcher applications concerning AI features of very large platforms are granted, refused or delayed, on what grounds, and how long does each stage take?
Who writes research on AI's social effects outside arXiv?One PhD chapter or paperLow to medium costPartly addressedNot yet measuredFirst-pass screen onlyAcross social-science and humanities journals, books, policy reports and arXiv, what share of research on AI's social effects comes from computer-science teams, social scientists, mixed teams, industry and government, and how has that share changed since 2014?
Whose conversations do data donations capture?One PhD chapter or paperNeeds a budget (medium or higher)Partly addressedWrong or missing validation targetFirst-pass screen onlyHow do people who donate their chatbot conversations to researchers differ from the developers' user bases, and how much do findings from donated data change once donors are weighted to the user population?

Living Debate LD-09

Does language-model use narrow what whole fields study, including the social sciences?A PhD projectLow to medium costPartly addressedNot yet measuredFirst-pass screen onlySince language models became widely used in 2023, has the range of topics covered by papers from adopting researchers shrunk relative to non-adopters, at the level of whole fields, and does the answer differ between the natural and the social sciences?
Does the convergence caused by writing with AI persist or compound over months of use?A PhD projectNeeds a budget (medium or higher)Partly addressedNot yet measuredFirst-pass screen onlyWhen the same people write or brainstorm with a language model repeatedly over a term or a year, does the similarity of their work to each other's grow, stay level or fade, and does it carry over into what they write without the model?
Is the diversity of what reaches the market falling, or only diversity inside experiments?A PhD projectNeeds a budget (medium or higher)Partly addressedNot yet measuredFirst-pass screen onlyIn markets for creative and intellectual work, such as self-published fiction, has the diversity of published works, of works that sell and of works that reach the top ranks changed since generative AI spread, beyond what changes in volume explain?
Does pooling different models keep restoring diversity as models converge?One PhD chapter or paperLow to medium costPartly addressedUntested beyond the original settingFirst-pass screen onlyAcross successive generations of language models from several providers, how much diversity of ideas does pooling models add compared with one model, and is that gain shrinking as models' outputs and errors become more alike?
Does AI writing help pull writers toward Western styles in languages other than English?Funded team projectNeeds a budget (medium or higher)No study foundGeographic or language coverageFirst-pass screen onlyWhen people write in their own language, not English, with AI suggestions, do their texts move toward the style and content of English-language or Western writing, and does this differ by language and by how much text in that language the model was trained on?
In science, is it the wording that converges or the ideas?One PhD chapter or paperLow to medium costPartly addressedWrong or missing validation targetFirst-pass screen onlyIn scientific papers and proposals written with language models, how much of the measured rise in similarity is shared wording and style, and how much is shared research questions, methods and claims, when the two are measured separately and validated against expert judgement?

Living Debate LD-10

Do AI answers in search cut clicks to outside sites on phones and outside the US?One PhD chapter or paperNeeds a budget (medium or higher)No study foundUntested beyond the original settingWhen AI answer layers in search (Google AI Overviews and AI Mode) are shown or hidden at random on people's own smartphones, in at least one non-US market and language, how much do they change clicks to external websites, zero-click searches, the kinds of sources visited and trust in the information, compared with the US desktop estimates?
Are AI answer layers narrowing or widening the range of sites people visit?A PhD projectNeeds a budget (medium or higher)Partly addressedNot yet measuredAmong people whose browsing is tracked across devices and platforms in 2025–26, does exposure to AI answers built into search (AI Overviews, AI Mode) and to standalone chatbots change the number, concentration and type of websites and news sources they visit over months, and does the change differ from what was found for early chatbot adoption?
Are AI search answers less faithful to their cited sources outside US English?Master's thesisLow to medium costPartly addressedGeographic or language coverageFor matched public-interest queries (news, health, elections) issued in several languages and country markets, how often are claims in Google AI Overviews and AI Mode unsupported by the pages they cite, judged claim by claim with native-speaker validation, and how do these rates differ from a US English baseline run through the same pipeline?
Does weeks of everyday AI-answer use make people check sources less?One PhD chapter or paperNeeds a budget (medium or higher)Partly addressedNot yet measuredWhen people are randomly assigned to search with AI answers as the default, or without them, for eight to twelve weeks of their normal information seeking, how do their source-checking behaviour, trust in AI answers, ability to spot wrong answers and reliance on AI for news and health questions change over time, and do early effects fade, persist or grow?
Do AI-content labels change belief and sharing on a live social platform?One PhD chapter or paperNeeds a budget (medium or higher)Partly addressedWrong or missing validation targetOn a live, open social platform, when labels saying that a post is AI-generated, or that it is AI-generated and potentially misleading, are applied at random to real political and news posts in users' feeds, how do they change actual reposts, likes and replies, and users' belief in the labelled claims, compared with unlabelled posts?
An updated meta-analysis of AI persuasion that separates attitudes from behaviourMaster's thesisLow costPartly addressedNot yet measuredPooling randomized experiments published from 2023 to 2026, including those testing 2025–26 frontier models, how large is the persuasive effect of AI messages and conversations, relative to no message and to human persuaders, and how does it vary with model generation, conversation format, message length, topic, country and outcome type (attitude, vote intention, behaviour, persistence at follow-up)?

Living Debate LD-11

Does public research compute change what academic AI researchers can do without industry?One PhD chapter or paperLow costPartly addressedCausal mechanism not isolatedAmong US academic teams that received National AI Research Resource (NAIRR) pilot allocations, compared with similar teams that did not, does access change reliance on proprietary model APIs, industry co-authorship and the share of compute-intensive work, and how much of the access delivered depends on private-sector in-kind resources?
How large is open-weight model use beyond a single routing platform?One PhD chapter or paperNeeds a budget (medium or higher)Partly addressedUntested beyond the original settingHow does the open-weight share of language-model use estimated from OpenRouter compare with shares estimated from other observable sources (other inference marketplaces and hosts, model downloads, self-hosting tool registries, and a survey of organisations' production deployments), and which population does each source actually represent?
Did UK public bodies switch cloud providers after egress fees were waived?Master's thesisLow costNo study foundNot yet measuredWhen UK public-sector cloud contracts came up for renewal before and after the 2024 egress-fee waivers and the CMA cloud investigation, how often did buyers move workloads to a different provider or add a second one, and did switching differ for workloads tied to Microsoft software licensing?
Do AI startups backed by a cloud giant end up running on that giant's cloud?One PhD chapter or paperLow to medium costNo study foundNot yet measuredAmong AI startups that raised money from Amazon, Microsoft or Google (directly or through their venture arms), compared with similar startups funded by independent investors, how much more likely are they to build on the investing firm's cloud and models, and does that tie persist or loosen in later funding rounds?
How much of the fall in AI prices is competition rather than cheaper technology?One PhD chapter or paperLow costPartly addressedCausal mechanism not isolatedFor language-model inference since 2023, how much of the fall in the price of a fixed capability level reflects lower serving costs (hardware and algorithmic efficiency) and how much reflects narrowing margins under competition, estimated by comparing prices for identical open-weight models across competing hosts with their estimated serving costs and with proprietary models of similar capability?
Which layers stay foreign when sovereign AI compute projects go live?Master's thesisLow costPartly addressedInstitutional practice unknownFor a fixed list of announced public or sovereign AI compute projects in the EU and the Gulf, how many reach operation by the dates they announced, and at each layer (chips, memory, data-centre operator, cloud software, models) which suppliers and which jurisdictions does the operating project depend on, compared with what was announced?

Living Debate LD-12

How often do people use GDPR rights over automated decisions, and what happens?One PhD chapter or paperLow costPartly addressedNot yet measuredAfter the Court of Justice's 2025 ruling that people subject to automated decisions are owed meaningful information about the procedure and principles actually applied, how many explanation and objection requests do data subjects make in a few high-exposure sectors (credit scoring, insurance, platform work), how do organizations answer them, and how many escalate to a data protection authority or court and with what outcome?
How many staff and how much money do national AI Act enforcers actually have?Master's thesisLow costPartly addressedInstitutional practice unknownFor each EU and EEA country, what staff numbers and budgets have been planned and actually provided for the authorities that enforce the AI Act, how do these compare with the tasks each authority has been given, and how do they compare with the resourcing of the same country's data protection authority?
Did signing the EU's voluntary AI code change what model providers actually do?One PhD chapter or paperLow costPartly addressedCausal mechanism not isolatedBetween before and after August 2025, did providers that signed the EU General-Purpose AI Code of Practice change their documented safety, transparency and copyright practices more than providers that signed only part of it or did not sign, when the same documents are scored with the same rubric over time?
China's AI filing register: how often are filings sent back, and whose models do apps use?One PhD chapter or paperLow to medium costPartly addressedInstitutional practice unknownUsing the successive public lists of filed generative-AI services and registered applications published by China's internet regulator, together with interviews with applicants and filing consultants, how often do filings go through rejection and rework before approval, how often do listed services later disappear, and how concentrated is the dependence of registered applications on a small number of filed base models?
Do EU delays of AI products line up with the AI Act's dates or with other EU rules?One PhD chapter or paperLow costPartly addressedCausal mechanism not isolatedFor AI products and features released from 2023 onwards by a broad set of providers (not only frontier labs), how often is availability in the EU delayed or withheld relative to the US, the UK and Switzerland, and do delays cluster around the dates when the AI Act's general-purpose model obligations applied (August 2025) and became enforceable (August 2026), separately from data-protection and competition-law events?
Do AI hiring-audit rules cost small employers more than large ones?Master's thesisLow costPartly addressedNot yet measuredUnder New York City's bias-audit law for automated hiring tools, how do the costs and forms of compliance (commissioning an audit, relying on a vendor-supplied audit, narrowing the tool's use to fall outside the law, or doing nothing visible) differ by employer size, and do these differences favour large employers and large tool vendors?