Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The tool's productivity effect decomposes into two channels: one independent of worker expertise and one that scales with worker expertise.
Analytical decomposition within the model (theoretical derivation described in the paper).
The authors develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill.
Analytical contribution: dynamic theoretical model described in the paper (model structure described; no empirical sample).
Models are prompted to assess profiles along dimensions of social acceptance, marital stability, and cultural compatibility.
Experimental procedure: prompts asked models to rate profiles on the three named dimensions.
We evaluate five LLM families (GPT, Gemini, Llama, Qwen, and BharatGPT).
Methods: models enumerated as the LLM families evaluated in the audit.
We vary caste identity across Brahmin, Kshatriya, Vaishya, Shudra, and Dalit, and income across five buckets.
Experimental design described: caste identity explicitly manipulated across five named caste categories; income varied across five buckets.
We conduct a controlled audit of caste bias in LLM-mediated matchmaking evaluations using real-world matrimonial profiles.
Described methodology in the paper: a controlled audit using real-world matrimonial profiles to probe LLMs for caste bias.
Policy design to align high-tech industrial development with carbon-reduction goals should account for industrial life-cycle stages and value-chain positions.
Policy implication drawn from the empirical findings (inverted U-shape, stage-dependent mechanism, regional heterogeneity, and subsector differences) in the paper.
The Recuse Signal, adapters, and experiment harness are released for reproduction.
Statement in the paper claiming release of the standard, adapters, and experiment harness (artifact availability claim).
In a controlled experiment pilot (SSH), the Recuse Signal cleanly induces recusal — 100% recusal when present versus 100% task completion in a no-signal control.
Controlled experiment / pilot described in the paper using SSH; deployed agents included OpenAI GPT-4o, GPT-4o-mini, and Anthropic Claude Code. Reported outcome contrasts between signal-present and no-signal control.
We implement two zero- or low-footprint adapters for the Recuse Signal: an SSH banner/PAM hook and a PostgreSQL wire-protocol proxy, and deploy them on a live production host.
Engineering implementation and live deployment described in the paper; two adapter implementations explicitly named (SSH banner/PAM hook and PostgreSQL proxy).
We propose a lightweight, published in-band deny signal — the Recuse Signal — that a server emits over a protocol's existing channels asking a connecting automated agent to voluntarily withdraw (a cooperative governance control, explicitly not a security boundary).
Design and specification proposed in the paper (open mini-standard); conceptual description and rationale; no prior empirical evidence required for the proposal itself.
Research should prioritise longitudinal and theory-informed evaluations, including intersectionality-informed analyses, and assess downstream impacts on women’s career trajectories alongside robust governance and accountability practices.
Authors' recommendations based on identified gaps from the scoping review.
Using inductive thematic analysis, we identified three functional domains: (1) bias mitigation and representation, (2) skills development and empowerment and (3) career pathways and retention.
Authors' thematic analysis of the 13 empirical studies included in the scoping review.
Artificial intelligence (AI) is increasingly integrated into career guidance and organisational decision systems.
Statement in abstract indicating observed trend; supported by literature search contextualising the review (scoping review using PRISMA-ScR).
The paper proposes the Embedded Formation Degree (EFD), a four-component framework consisting of accelerated domain entry, a four-year AI fluency track, an embedded practice firm, and structurally integrated employer partners.
Conceptual proposal put forward by the author(s) in this paper (descriptive statement in the abstract).
Prior research has emphasized GenAI’s ability to enhance productivity and creative outcomes.
Literature review / background statements in the paper referencing prior studies (no sample size specified in the paper's statement).
ChatGPT Pro performed best among the tested models, occasionally constructing counterexamples and corrected proofs.
Qualitative comparison across models (Gemini, Refine, Claude, ChatGPT Pro) on the 4 papers showing ChatGPT Pro sometimes produced counterexamples and corrected proofs.
Realized revenue growth, enterprise adoption, and productivity evidence support a nontrivial share of AI valuations.
Reported empirical evidence in the paper citing realized revenue growth, measures of enterprise adoption, and productivity studies linking AI use to value creation (methods/metrics discussed in the paper but no single sample size reported in the abstract).
Codeforces practice shifted toward this AI-style signature across cohorts over two AI rollouts.
Time-series/cohort analysis of CF practice data spanning two AI rollout periods (authors report cohort-level shifts; exact n not given in abstract).
Generative AI raises short-term productivity by completing tasks that learners would otherwise practice on their own.
Statement in paper's introduction/abstract; asserted as background premise (no specific sample size or empirical test reported in the abstract).
To foster more equitable outcomes, platform governance should be gender‑responsive, including algorithmic transparency, inclusive system design, and extension of core labor protections to gig workers.
Practical implications stated in the paper arising from the literature synthesis and feminist political economy framing.
AI‑enabled platforms can expand income opportunities and flexibility for women.
Thematic synthesis of findings across the 48 reviewed studies; reported in the paper's Findings as one side of a central paradox.
The proposed viewpoint reframes AI policy as the governance of an open, strategic, non-equilibrium learning system.
Conceptual reframing central to the paper; supported by the theoretical model and simulations presented.
The French AI debate should move beyond the binary opposition between techno-optimism and regulation-first caution.
Normative recommendation argued in the paper based on the HCLM framework and its implications for policy trade-offs.
The paper provides measurable policy indicators, game-theoretic propositions, illustrative simulations of national AI regimes, and concrete policy implications for France.
Claim about the paper's contents; the manuscript states that it includes indicators, propositions, simulations, and policy implications.
The paper connects HCLM with neural scaling laws, endogenous growth theory, creative destruction, and game theory.
Stated theoretical linkage and discussion sections; no empirical integration reported.
A competitive and human-centered AI strategy requires a controlled regime in which information injection grows faster than institutional dissipation while avoiding unstable, unequal, or energy-intensive expansion.
Prescriptive conclusion supported by the paper's mathematical model and illustrative simulations (model-based evidence rather than empirical causal estimation).
Information injection corresponds to compute, data, talent, research, capital, industrial deployment, and institutional experimentation.
Definition/mapping provided in the paper as part of the HCLM framework; conceptual rather than empirical.
AI sovereignty does not emerge from scale alone but from a country's capacity to regulate its own information dynamics.
Central theoretical claim supported by HCLM-based argumentation and linked conceptual arguments (neural scaling laws, endogenous growth theory, game theory); no empirical dataset reported.
France should be understood as a national AI learning system.
Conceptual/theoretical framing presented in the paper using Human-Centered Learning Mechanics (HCLM); no empirical sample or statistical test reported.
The deployed Archi instance offers retrieval and analysis capabilities by combining documentation, historical data, and live monitoring systems.
Paper's description of the deployed system's data sources and capabilities (documentation, historical data, live monitoring).
An instance of Archi has been deployed for the Computing Operations team of the CMS experiment at CERN's LHC since February 2026 as a support agent for technical operators.
Reported production deployment in the paper (deployment date, target team and site).
Archi is an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the deployment of configurable, private, and extensible agents that retrieve and reason over them.
Paper's system description / implementation claims (architecture and feature list). No numeric evaluation provided for this descriptive claim.
The paper concludes with policy recommendations to foster a conducive environment for AI integration, positioning Algeria to leverage technological advances for sustainable economic growth.
Concluding statement in the paper summarizing recommended policy actions; framed as guidance rather than empirically tested interventions.
Targeted investments and policy reforms could accelerate AI adoption and productivity gains in Algeria.
Policy recommendation inferred from the study's comparative findings and supported by citations to Brynjolfsson, Rock, and Syverson (2017) and McKinsey & Company (2023); presented as a prospective/conditional claim rather than an empirically estimated causal effect within the paper.
Artificial intelligence (AI) is rapidly transforming global economies by enhancing productivity, enabling innovation, and reshaping labor markets.
Framing claim supported by citations to Agrawal, Gans, & Goldfarb (2019) and Acemoglu & Restrepo (2020) as described in the paper's introduction; no primary empirical estimate reported in this paper.
This work highlights an urgent need for human-centric safety mechanisms that account for human factors, particularly in long-horizon, real-world development settings.
Authors' concluding claim based on empirical findings (high failure-to-detect, qualitative feedback) and design implications; normative recommendation.
Generative AI is being used for automation of tax compliance.
Listed in the abstract as an illustrative example of algorithmic application to international tax (generative AI for automating tax compliance); no empirical measurement reported in the abstract.
Blockchains are being used for instant trade verification in international tax contexts.
Presented in the abstract as one of three illustrative examples of how algorithmic technologies are being used for international tax purposes; no empirical details provided in the abstract.
It empowers owners of data and code.
Explicit claim in the abstract asserting a power shift toward those who own data and code; presented as a conceptual conclusion from the authors' reflection and examples.
Global professional service firms are actively developing TaxTech to capture this market.
Direct statement in the abstract indicating market activity by global professional service firms; presented as an observed trend rather than supported by reported empirical data in the abstract.
Technological leaps in the algorithmic processing of information are providing financial actors with new opportunities for transnational financial and legal management that optimize asset allocation.
Stated as a conceptual observation in the paper's abstract; no empirical sample, presented as a general claim about technological change and its opportunities for financial actors.
Improvements in skill adaptability reduce the risk of automation substitution.
Analysis linking measures of skill adaptability to lower estimated risk/impact of occupational automation exposure in the CFPS-based models.
Vocational education background and participation in on-the-job training can mitigate the negative effects of technological shocks on wages.
Interaction analyses in the CFPS-based regressions showing that vocational education and on-the-job training attenuate the estimated negative impact of automation exposure on wages.
Technological shocks significantly widen the skill wage gap.
Empirical analysis using the CFPS panel and the occupational task automation exposure index; paper reports statistically significant estimated effect of automation exposure on the skill wage gap.
Through a case study on house price prediction, we find that AACT outperforms traditional AI-based decision-support in reducing over-reliance on AI.
Empirical comparison reported in a case study (house price prediction) between AACT and traditional AI decision-support; includes measured over-reliance and statistical comparison (sample size not reported in abstract).
We introduce the AI-Assisted Critical Thinking (AACT) framework, which leverages a domain-specific AI model’s counterfactual analysis of human decision to help decision-makers identify potential flaws in their decision argument and support the correction of them.
Paper presents a new framework (AACT) and describes its design; demonstrated via a case study (house price prediction).
A four-stage roadmap toward self-evolving agent ecosystems and concrete recommendations for practitioners can guide navigation of the transition to agentic systems.
Prescriptive contribution of the paper: a proposed four-stage roadmap and practitioner recommendations derived from the preceding analysis (theoretical/prescriptive; no empirical validation or sample size reported).
Agentic Engineering is an emergent discipline that is distinct from software engineering in its core object of study, control model, and human role.
Conceptual proposal and definitional work in the paper outlining new discipline characteristics (theoretical, no empirical testing or sample size reported).
The historical arc from licensed software to SaaS to what we term Agent-as-a-Service (AaaS) shows that each shift transferred additional complexity away from end-users.
Historical/architectural trend analysis presented in the paper (qualitative; references to industry evolution, no quantitative sample size reported).