Evidence (8974 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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We introduce AgentDS, a benchmark and competition designed to evaluate both AI agents and human-AI collaboration performance in domain-specific data science.
Paper describes the creation of the AgentDS benchmark and an associated competition as the study's primary methodological contribution.
Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significantly automated data science workflow.
Statement in the paper referencing recent developments in LLMs and AI agents; presented as motivation rather than validated empirically within the paper.
Data science plays a critical role in transforming complex data into actionable insights across numerous domains.
Background statement in the paper (no empirical test or dataset provided to support this claim).
End-to-end verified pipelines can produce provably correct code from informal specifications.
The paper surveys early research demonstrating pipelines that go from informal specifications to formally verified code; the provided text does not include experimental sample sizes or benchmarks.
AI-generated postconditions catch real-world bugs missed by prior methods.
Surveyed early research asserted by the paper indicating empirical instances where AI-generated postconditions found bugs that other methods missed; no numeric details provided in the excerpt.
Interactive test-driven formalization improves program correctness.
Paper surveys early research that reportedly demonstrates this effect (described as 'interactive test-driven formalization that improves program correctness'); the excerpt does not include specific study details or sample sizes.
The central bottleneck is validating specifications: since there is no oracle for specification correctness other than the user, we need semi-automated metrics that can assess specification quality with or without code, through lightweight user interaction and proxy artifacts such as tests.
Analytical claim and research agenda item in the paper; motivates need for new metrics and interaction designs. No empirical validation or sample size reported in the excerpt.
Intent formalization offers a tradeoff spectrum suitable to the reliability needs of different contexts: from lightweight tests that disambiguate likely misinterpretations, through full functional specifications for formal verification, to domain-specific languages from which correct code is synthesized automatically.
Conceptual framework proposed in the paper describing a spectrum of specification formality; presented as an argument rather than an empirical finding, with no sample sizes provided in the excerpt.
Intent formalization — translating informal user intent into checkable formal specifications — is the key challenge that will determine whether AI makes software more reliable or merely more abundant.
Normative argument presented by the authors as the central thesis of the paper; no empirical study or sample size cited in the provided text.
Agentic AI systems can now generate code with remarkable fluency.
Authoritative assertion in the paper based on contemporary observations of large code-generating models; no empirical sample size or benchmark numbers reported in the text provided.
The study implies policy actions to promote high-quality development based on the finding that innovation and the digital economy now play larger roles in growth.
Authors' discussion/conclusion drawing policy implications from empirical findings (declining capital elasticity, rising TFP and digital economy contribution).
Overall, China's growth model shifted over 2010–2022 from being investment-driven to being innovation-driven.
Synthesis of results: declining capital elasticity, rising TFP contribution, substantial share of digital economy in TFP, and regional patterns reported by the study.
The study's method is novel because it uses both migrant worker monitoring data and digital-economy proxy indicators, giving a more accurate picture of how labor quality and technological progress affect each other.
Author-reported methodological description: extended Cobb–Douglas approach combined with quality-adjusted labor measures derived from migrant worker monitoring data and proxy indicators for the digital economy.
Regional analysis shows coastal regions have been driven by innovation, with an estimated (innovation) coefficient of approximately 0.31.
Regional decomposition/estimation reported in the paper's analysis of coastal vs inland regions using the extended production function and digital/labour-quality measures.
The digital economy accounted for 40% of the observed increase in TFP (i.e., made up 40% of the TFP contribution).
Attribution within the growth decomposition from the extended production function, where digital economy indicators are included and their contribution to TFP is estimated.
The contribution rate of total factor productivity (TFP) rose from 18% to 26% between the earlier and later periods.
Decomposition of growth using the extended Cobb–Douglas production function for China over 2010–2022, reporting TFP contribution rates for the two periods.
TDAD (Test-Driven Agentic Development) combines abstract-syntax-tree (AST) based code-test graph construction with weighted impact analysis to surface the tests most likely affected by a proposed change.
Description of the tool/methodology and its implementation (TDAD is presented as an open-source tool in the paper).
PIER is an offline reinforcement learning framework that learns fuel‑efficient, safety‑aware routing policies from physics‑calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator.
Methodological description of PIER in the paper: offline RL trained on environments constructed from AIS and reanalysis data; no online simulator used for policy learning (implementation details provided).
Bootstrap 95% confidence interval for PIER mean CO2 savings relative to great-circle routing is [2.9%, 15.7%].
Bootstrap analysis applied to the 2023 AIS validation results (840 episodes per method) producing the stated 95% CI for mean percent savings.
PIER reduces per‑voyage fuel consumption variance by a factor of 3.5 (p < 0.001).
Statistical comparison of per-voyage fuel variance between PIER and baseline routing on 840 episodes per method from 2023 AIS data; significance reported with p < 0.001.
On the LoCoMo benchmark, the architecture achieves 74.8% overall accuracy.
Benchmark evaluation reported in the paper using the LoCoMo benchmark with a reported overall accuracy of 74.8%.
Adversarial governance compliance was 100%.
Adversarial compliance testing reported in the paper (linked to the adversarial query experiments); reported compliance = 100%.
There was zero cross-entity leakage across 500 adversarial queries.
Adversarial testing reported in the paper: 500 adversarial queries used to test cross-entity leakage; result = zero leakage.
Progressive context delivery yielded a 50% token reduction.
Reported experimental result in the controlled experiments indicating token usage reduction from progressive delivery = 50%.
Governance routing precision was 92% in the experiments.
Reported experimental metric from the controlled experiments (N=250, five content types) showing governance routing precision = 92%.
The system achieved 99.6% fact recall (with complementary dual-modality coverage) in the controlled experiments.
Reported experimental result from the controlled experiments (N=250, five content types) as stated in the paper.
Immediate practical steps include improved documentation, stakeholder audits, and multi‑metric evaluation; medium‑term steps include standards for participatory evaluation and tooling for transparency and monitoring; long‑term steps include institutional governance, interoperable safety APIs, and public‑interest evaluation infrastructure.
Prescriptive roadmap in the paper based on conceptual analysis and prior literature; these are recommended policy/program milestones rather than empirically validated interventions.
Transparency (detailed documentation of data, objectives, evaluation processes, and deployment constraints; audit and contest mechanisms) is a necessary mechanism for accountable alignment.
Normative and practical argumentation supported by prior work on model cards, documentation standards, and auditing; no new audits are presented in the paper.
Pluralistic evaluation—using multiple, diverse evaluation criteria and stakeholder‑informed metrics rather than single aggregated alignment scores—will better capture the values and harms at stake.
Argumentative rationale and literature synthesis advocating multi‑metric evaluation approaches; examples from prior evaluation critiques are referenced rather than new empirical comparison.
The Flourishing–Justice–Autonomy (FJA) framework should guide alignment efforts, emphasizing (1) Flourishing (human well‑being and meaningful opportunities), (2) Justice (distributional fairness and protection of vulnerable groups), and (3) Autonomy (informed choice and user control).
Prescriptive proposal grounded in conceptual analysis and synthesis of ethical and technical literature; the paper defines and motivates the three principles as its core normative contribution.
The report issues seven policy recommendations grouped into three goals: (1) improve understanding of the emerging threat, (2) strengthen defenses, and (3) ensure responsible development and deployment.
Policy synthesis based on threat analysis and governance review (report-authored recommendations; descriptive).
The study's strengths include multimethod triangulation, a very large behavioral dataset (150 million interactions), and controlled simulation experiments informed by empirical observation.
Methods reported: mixed‑methods sequential design with (1) 6‑month lab ethnography (n = 23), (2) computational analysis of 150 million customer interactions, and (3) empirically grounded agent‑based simulation experiments.
The Algorithmic Canvas is an operational medium where segmentation, targeting, and positioning parameters co‑evolve through iterative human–AI collaboration.
Design and implementation described in the study; observation of Canvas‑mediated interactions during a 6‑month lab ethnography inside a Fortune 500 company (n = 23).
Autopoietic STP + Algorithmic Canvas approach is 44% more resilient to market shocks than traditional, process‑based STP (p < 0.01).
Agent‑based simulations and comparative analyses informed by empirical calibration; supported by large‑scale behavioral data (150 million customer interactions) and simulation experiments. Statistical test reported with p < 0.01. Exact number of simulation runs and full test details not specified in the summary.
Research priorities include empirically quantifying AI's effects on productivity, wages, inequality, and environmental costs; developing standardized sustainability and governance metrics; and evaluating regulatory impacts on innovation and welfare.
Stated research agenda based on gaps identified in the narrative review; identifies directions for future empirical work rather than presenting new empirical findings.
AI has progressed from symbolic systems to data-driven, generative architectures and large-scale computational infrastructures, becoming a foundational technology across sectors.
Narrative synthesis of historical and technical literature across AI research and innovation studies; qualitative tracing of architectural shifts (symbolic → statistical → deep learning/generative models) and increased deployment across industries. No original empirical measurement or sample size reported in this paper.
The main results are robust to inclusion of controls and a range of heterogeneity and moderation checks, supporting that findings are not driven by simple time trends or obvious confounders.
Reported robustness checks in the staggered-DID framework (control variables, alternative specifications, subgroup tests) and discussion of parallel-trends assumption.
Implementation of urban green data center pilot policies leads to measurable improvements in firms' energy utilization efficiency.
Staggered-adoption difference-in-differences (DID) using an unbalanced firm–year panel of Chinese A-share listed firms linked to prefecture-level cities (2012–2024); treatment is timing/location of urban green data center pilot designation; results reported as statistically significant and robust to controls and alternative specifications.
Policy recommendations include standards on explainability, audit trails, certification for finance/tax AI systems, stronger data governance, and public–private coordination to update regulatory guidance.
Paper's policy and governance recommendations drawn from case findings and literature synthesis; prescriptive content rather than evaluated interventions.
Deployments should build governance, explainability, and auditability into systems and start with pilots on high-volume, well-structured tasks before scaling.
Paper recommendations based on case experience and analytic framing; advocated strategy rather than empirically validated at scale within the paper.
To mitigate risks and realize benefits, AI systems in finance/tax should combine AI with human-in-the-loop controls and clear escalation paths.
Prescriptive recommendation grounded in case lessons and literature on safe AI deployment; presented as a best-practice guideline rather than tested intervention.
Technical building blocks leveraged in these deployments include large language models (LLMs), OCR plus structured information extraction, retrieval-augmented generation (RAG) and knowledge bases, and process automation/RPA.
Explicit technical characteristics section and case descriptions in the paper identify these components as core to implementations.
Generative AI is used for risk control and audit functions, including real-time monitoring, fraud detection, KYC/AML screening, and automated exception reporting.
Reported use-cases in the two case organizations and corroborating industry reports discussed in the literature review portion of the paper.
For tax declaration, generative AI enables extraction of tax-relevant facts from invoices and contracts, drafting of tax returns, compliance checks, and scenario simulations.
Case examples and literature synthesis describing OCR + information extraction and LLM-assisted drafting workflows used in practice.
Generative AI is applied to fund management tasks such as cashflow forecasting, anomaly detection, and automated workflows for payments and collections.
Case descriptions and technical mapping in the paper showing implementations at the sharing center and professional services firm level.
Accounting automation use-cases include automated bookkeeping, reconciliations, journal entry suggestion, and error detection using LLMs and document understanding.
Detailed scope mapping and case examples in Xiaomi and Deloitte illustrating these accounting applications; supported by literature review of technical capabilities.
Realizing those AI-driven gains in Vietnam requires legal and institutional redesigns.
Close reading of Vietnam's constitutional provisions, administrative statutes, procedural rules and judicial doctrine (doctrinal legal analysis) combined with comparative lessons from other jurisdictions; no quantitative data.
Rigorous research priorities include randomized controlled trials with long-run follow-ups, cost-effectiveness studies, structural adoption models, and validated metrics for feedback quality and learning durability.
Actionable research recommendations produced by the 50-scholar interdisciplinary meeting; prescriptive synthesis rather than empirical results.
An asynchronous sliding-window engine treats the GPU as a sliding compute window and overlaps GPU computation with CPU-side parameter updates and multi-tier I/O to hide data movement and synchronization overheads.
System design and implementation described in the paper: an asynchronous runtime that coordinates GPU kernels, CPU updates, and multi-tier I/O. This is a design/implementation claim rather than a measured outcome; the summary links the design to performance improvements.
The A-ToM mechanism operates by estimating a partner's likely ToM order from interaction history and using that estimate to predict the partner's next action which then informs the agent's policy choices.
Method description and implementation details provided in the paper: estimator over ToM orders based on past interactions + conditional action prediction feeding into decision-making; validated in the reported experiments.