Evidence (4189 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
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We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counterfactual risk toll computed against a contractually fixed safe default, inside an explicit underwriting boundary.
Theoretical proposal and formal description of an actuarial framework presented in the paper (architectural/axiomatic exposition). No empirical sample or experiment reported.
This study proposes a Workforce Resilience Governance Framework (WRGF) that includes task-level exposure assessment, human augmentation design, reskilling, redeployment, transparent communication, psychological safety, workforce impact accountability, and policy alignment.
Conceptual framework proposed by the authors in the paper (design/proposal; no empirical test described in the excerpt).
The paper concludes with policy recommendations for accelerating human-centred AI integration in public-sector HRM.
Stated conclusion and policy recommendations section in the paper; recommendations derived from empirical findings.
Access to modern digital tools positively moderates AI uptake.
Reported moderation/interaction effects in regression/path analysis indicating that access to modern digital tools is associated with higher AI adoption/uptake; exact effect size not specified in summary.
Holding a managerial position is the strongest predictor of active AI adoption (OR = 1.609).
Reported odds ratio from the binary logistic regression for role/position predictor (managerial status) predicting active AI adoption; OR = 1.609.
Internal HR factors exert a stronger influence on perceived HR effectiveness (β = 0.463) than external factors (β = 0.227).
Reported standardized (?) path/regression coefficients from OLS/path analysis linking internal and external HR quality indices to perceived HR effectiveness; coefficients β = 0.463 and β = 0.227 respectively.
Future evaluations should use artifact-level denominators, reproducible parsing rules, correction taxonomies, and independent coding of governance events.
Authors' recommendations based on methodological lessons from this structured self-observed implementation case study and observed parsing/governance challenges.
We evaluate collaborative performance from consensus-based routing among self-interested heterogeneous agents in AgentSociety on real-world datasets.
Empirical evaluation / experiments using real-world datasets to measure collaborative performance under consensus-based routing among heterogeneous agents.
We characterize the Nash equilibrium showing that agent payoffs are reflective of their marginal contributions.
Analytical game-theoretic characterization/proof of Nash equilibrium in the paper.
The mechanism incentivizes agents to selectively disclose information to their neighbor agents when doing so aligns with their self-interest, in order to garner influence.
Theoretical analysis and mechanism design arguments (and possibly supporting simulations) within the paper.
Delegation to more competent neighbor agents is incentive compatible and naturally generates multi-agent routing path by consensus.
Formal theoretical proof/analysis presented in the paper (analytical/theoretical result).
We propose AgentSociety, a mechanism that enables decentralized agentic collaboration grounded in liquid democracy and information diffusion from social choice theory.
Description and design of the AgentSociety mechanism in the paper (mechanism proposal / system design).
AI assistance can stabilize an overloaded workflow only when (i) the fraction of tasks handled by AI exceeds a critical threshold, and (ii) the human attention required for review and expected rework is lower than the attention required for manual completion.
Formal analytical conditions derived from the paper's queueing model (model-based theoretical result; no empirical sample reported).
LLM-assisted systems make candidate generation, code comprehension, harness construction, proof-of-impact drafting, and report preparation cheaper at codebase scale.
Argument supported by analysis using public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations (qualitative and illustrative examples; no sample size reported in the provided text).
The paper calls for action by stakeholders to consider human and environmental moderators when adopting AI.
Policy/recommendation statement in the paper's conclusion/abstract; normative recommendation rather than empirical finding.
We revise the existing framework to redefine effective organizational determinants and shed light on practical implications including industry and education.
Authors' proposed theoretical revision of an existing framework and discussion of implications; presented as a conceptual contribution within the paper.
Most practitioners assume that AI brings productivity boosts owing to enhanced technical capabilities.
Statement of common practitioner belief reported by the authors in the paper's framing; no supporting survey or sample reported in the abstract.
A profile-driven approach places humans and AI systems on shared scales, supporting comparisons that are predictive of novel-task performance, explanatory of why agents succeed or fail, and auditable.
Claim about anticipated benefits of the proposed profile-driven approach presented in the paper (theoretical argument; no empirical results reported).
Suitability evaluations for task-assignment should be profile-driven — based on assessments that infer latent constructs such as capabilities and propensities from observed performance.
Core proposal of the position paper (conceptual/methodological recommendation; no empirical pilot or validation reported).
As AI is integrated into the workplace, organisations increasingly face allocation decisions between human and machine workers, and these decisions are increasingly made or assisted by algorithms.
Position paper / conceptual argument in the paper's introduction (no empirical sample or quantitative data reported).
A human-centred approach underpinned by ongoing reskilling and ethical governance is vital for sustainable workforce evolution in the Indian IT sector.
Authors' policy/recommendation derived from their literature synthesis and thematic analysis (qualitative conclusion).
The paper introduces a conceptual framework for hybrid intelligence within the Indian IT sector.
Authors present a new conceptual framework as part of this qualitative research article (conceptual contribution).
Collaboration between humans and AI enhances decision-making, efficiency, and innovation.
Reported result from thematic evaluation of literature and secondary data (qualitative synthesis). No sample size or quantified effect provided.
AI improves overall organisational productivity.
Authors' synthesis of peer-reviewed studies and secondary data indicating productivity impacts (qualitative literature review). No quantitative sample size reported.
AI increases human capacities.
Conclusion from comprehensive analysis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No primary sample size reported.
The aim is to keep autonomous agency composable while keeping accountability non-negotiable, so that coordination itself can become shared infrastructure for a human-AI society that is open, pluralistic, and governable.
Stated design/ethical objective in the paper; normative claim about intended social and governance outcomes rather than an empirically validated result.
FP is designed to wrap and bridge existing protocols rather than replace them, enabling incremental adoption while reducing integration and governance overhead.
Design rationale/claim in the paper about interoperability and incremental adoption strategy; no empirical deployment, integration case studies, or measured overhead reductions presented.
FP treats policy, provenance, and audit as first-class concerns.
Design/architectural claim in the paper stating that policy, provenance, and audit are prioritized within FP; no empirical compliance or audit trials presented.
FP provides economic primitives for metering, receipts, and settlement.
Design claim in the paper listing economic primitives as part of FP; no deployment or economic experiments reported.
FP supports native multi-party organization and event-based collaboration.
Feature/architecture claim in the paper describing native support for multi-party organization and event-driven collaboration; no empirical evaluation or user studies provided.
FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations.
Design specification/feature claim in the paper describing FP's data and entity model; no empirical interoperability study reported.
This paper introduces the Foundation Protocol (FP), a graph-first coordination layer for an emerging human-AI society.
Claim of authorship/introduction in the paper; architectural/design proposal rather than an evaluated system.
Agents need to form reliable relationships, organize multi-agent work, exchange value, support an AI economy, and stay safe and accountable under real-world oversight.
Normative/requirements statement in the paper describing necessary capabilities for scaled multi-agent systems; no empirical validation or experimental data provided.
Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another.
Statement in the paper's introductory/abstract text presenting an observed trend; conceptual/qualitative claim without empirical data or measured sample.
European AI companies increasingly face differing regulatory expectations across global markets, and European institutions should provide structured support (advisory mechanisms, regulatory guidance, dialogue with partner jurisdictions) to help companies navigate emerging compliance requirements abroad.
Combined descriptive claim and policy recommendation; the text asserts increasing regulatory asymmetry faced by firms but provides no empirical data or firm-level survey evidence.
Systematic monitoring of global regulatory developments (for example through foresight functions within the European Commission or the AI Office) would help anticipate regulatory divergence and support future adjustments to European governance frameworks.
Policy recommendation advocating institutional monitoring mechanisms; argumentative justification rather than empirical demonstration in the text.
European regulators should monitor whether conversational systems begin to assume intermediary or gatekeeping roles within digital ecosystems and consider how existing platform governance frameworks might apply.
Policy recommendation advocating monitoring and potential regulatory application; no empirical study in text demonstrating current gatekeeping behavior.
Risk assessments and auditing standards should explicitly examine interaction design, including engagement optimisation mechanisms, recommendation loops, and other features that may encourage behavioural influence or dependency.
Normative recommendation arguing current frameworks focus mainly on outputs; no empirical evaluation or sample reported.
European institutions (in particular the European AI Office) should issue guidance on how systems designed for sustained social or emotional interaction should be assessed in the implementation of the AI Act.
Policy recommendation contained in the text; prescriptive argument rather than an empirical finding; no supporting data or empirical evaluation provided.
Existing regulatory frameworks will need to consider risks that arise not only from system outputs but also from longer-term patterns of human–AI interaction.
Normative recommendation based on the document's argument that conversational AI generates risks through sustained interaction; no empirical method or data reported.
The study advances multilevel propositions and outlines a research agenda for examining legitimacy in hybrid human–AI decision systems.
Paper presents multilevel theoretical propositions and a suggested agenda for future empirical research (conceptual contribution; no empirical validation reported).
Human judgment remains essential for contextual interpretation and accountability in hybrid human–AI decision systems.
Conceptual claim advanced through theoretical argumentation and literature references in the paper (no empirical sample reported).
Legitimacy of AI-enabled decisions depends on transparency, explainability, and perceived fairness.
Conceptual argument and literature synthesis in the paper emphasizing transparency, explainability, and fairness as determinants (no empirical sample reported).
AI enhances efficiency and consistency in organizational decision-making.
Theoretical claim supported by referenced literature and conceptual argumentation within the paper (no empirical test or sample reported).
Procedural, distributive, and cognitive legitimacy are key dimensions of decision legitimacy in AI-enabled organizations.
Conceptual development in the paper drawing on institutional theory, socio-technical systems, and behavioral decision-making; literature synthesis and theoretical argumentation (no empirical sample reported).
Accountability assets are complementary assets that make AI-supported outputs legitimate, auditable, reviewable, and assignable to a responsible party.
Conceptual definition and development in the paper; supported by illustrative domain examples but no empirical validation.
Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seemingly accelerating modularization and organizational disaggregation.
Conceptual/theoretical argument in the paper; theory development and illustrative examples across domains (document processing, legal services, audit, clinical decision support, procurement). No empirical sample or quantitative test reported.
This paper provides new evidence on AI adoption from a non-US context by leveraging German firm-level data (ifo Business Survey).
Use of a large German business survey (ifo Business Survey) and analysis of AI adoption patterns among German firms.
AI is expected to have positive long-term productivity impacts for different sectors of the German economy.
Assessment of potential productivity impacts using firm-level survey responses about expected long-term benefits of AI (forward-looking/expectation-based analysis).
The increase in AI usage from 2023 to 2024 was particularly pronounced in manufacturing and services sectors.
Sectoral breakdown of ifo Business Survey firm-level data showing higher increases in reported AI usage for manufacturing and services compared with other sectors.