Evidence (3470 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
Remove filter
Output-stage risks include authenticity/deception concerns, attribution and reuse-rights disputes, reputational harms, and broader societal impacts from abundant generated media.
Review of empirical studies on media authenticity, legal cases, and policy analyses included in the narrative review.
Process-stage risks include governance of model development, control over deployment, transparency, auditing, and operational safety.
Conceptual synthesis of technical governance literature and policy reports cited in the narrative review.
Input-stage risks include concerns about consent, copyright, representativeness, bias, provenance and data ownership for training material.
Synthesis of legal and policy literature and documented legal cases/statutes related to training data and IP/privacy issues (secondary sources only).
Generative audiovisual AI poses material ethical, control, transparency and legal challenges across three stages — input (training data), process (development & deployment), and output (use of artifacts).
Conceptual three-stage framework built from comparative review of literature, legal cases/statutes and policy reports described in the paper.
Limitations of the study include potential selection bias in reviewed sources and contingency of conclusions on evolving legal decisions and technology developments.
Author-stated limitations section within the paper; qualitative acknowledgement rather than empirical bias assessment.
Output-stage risks include challenges to authenticity and provenance, erosion of trust (deepfakes and misinformation), and potential legal liability for harms caused by generated content.
Synthesis of technical papers on deepfakes, legal analyses of liability, and policy reports referenced in the review; no original incident dataset or quantitative prevalence estimate included.
Input-stage risks include copyright infringement, lack of consent, poor data provenance, and biases/representational harms encoded in training datasets.
Review and synthesis of academic and legal literature on training data issues; examples and case law discussed, but no original dataset audit or sample counts provided.
Use of these models faces significant ethical, control, transparency, and legal challenges across three stages—input (training data), process (development/control), and output (generated artifacts).
Framework constructed from interdisciplinary literature (technical, ethical, legal sources) and review of statutes/judicial approaches; qualitative synthesis rather than primary data.
Results reflect small-scale e-commerce use cases; external validity to larger firms, other sectors, or more complex tasks is not established.
Scope of deployments limited to small-scale e-commerce settings as stated in methods; no cross-sector or large-firm samples reported in summary.
The study's evidence is observational rather than randomized controlled trials, so causal estimates about productivity impacts are suggestive rather than definitive.
Declared study design: applied experimentation and observational analysis of deployments (no randomized assignment); methods section explicitly notes observational limitation.
Integrating AI raises questions of accountability, transparency, fairness, privacy, and bias; managerial responsibility includes governance design, validation, and audit of AI decisions.
Normative and governance-focused synthesis citing ethical frameworks and illustrative cases; identifies governance tasks and validation/audit needs rather than empirical prevalence rates.
Deficits in governance, auditing, and interpretability constrain the safe deployment of generative AI in firms.
Synthesis of industry reports and conceptual literature noting gaps in governance and interpretability; no quantitative governance dataset reported.
Algorithmic biases in generative AI can amplify and codify discriminatory patterns in organizational decisions.
Extensive literature on algorithmic bias synthesized in the review and applied to generative models; case examples referenced.
Generative AI use introduces significant organizational risks including data privacy breaches and leakage when models or third‑party services are used.
Conceptual analysis and references to documented incidents and industry reports within the review; no single aggregated incident dataset provided.
The study is limited by being a single-domain (CMM) case study with a likely modest sample size and dependence on specific AR hardware and MLLM capabilities; further validation across other machines and larger samples is needed.
Authors note these limitations in their discussion; the summary explicitly lists single-case domain, likely modest sample size, and dependency on particular hardware/MLLM as limitations.
Governing-logic stability uncertainty (whether decision logic or objectives remain stationary) is a distinct risk posed by agentic AI.
Conceptual argument and proposed taxonomy; no empirical tests reported.
Epistemic grounding uncertainty (uncertainty about how/why an AI produced a particular output) increases with agentic AI.
Literature synthesis on model-level opacity and causal explanation limits; conceptual reasoning in the paper.
Behavioral trajectory uncertainty (difficulty predicting long-run actions) is a primary form of uncertainty introduced by agentic AI.
Conceptual classification and argument; proposed as one of three principal uncertainties; no empirical estimation.
Integration cost: AI-generated outputs often require human revision, testing, and manual integration into existing systems.
Reported practitioner experience and observed practices from the field study at Netlight; authors note time and effort spent on revision and integration; no quantitative time-cost estimates provided.
AI systems lack full project context, design rationale, and long-term constraints, creating context gaps for development tasks.
Interviews and workflow observations at Netlight where practitioners reported contextual limitations of AI tools; qualitative examples provided; single-firm qualitative evidence.
AI outputs commonly contain errors and hallucinations: generated code can be incorrect, incomplete, or misleading.
Practitioner reports and observed interactions with AI tools documented in the Netlight qualitative study; specific instances and practitioner concerns described in the paper; no quantitative error rates provided.
Integration and engineering complexity (legacy systems, privacy/compliance pipelines, multi-channel platforms) is a persistent barrier to deployment.
Industry case studies and practitioner reports synthesized in the review documenting integration challenges; no systematic cost accounting or sample sizes presented.
Hallucinations and factual errors from generative AI can damage service quality and customer trust.
Documented failure cases and empirical reports from the literature aggregated by the review; no novel incident count or experimental data in this paper.
Generative AI is susceptible to social and representational biases and to factual errors or hallucinations; it lacks tacit, contextual domain expertise.
Documented examples in the literature of biased outputs and hallucinations; controlled evaluations and audits of model outputs; qualitative reports highlighting lack of tacit knowledge in domain-specific tasks.
The quality of AI-generated outputs is highly variable; models frequently produce mediocre but plausible-sounding content that requires human filtering.
Multiple user studies and qualitative reports documenting variability in output quality and the need for human curation; outcome measures include error rates, user-rated quality, and time spent vetting.
Privacy concerns, regulatory/compliance issues, biased or opaque models, and the need for change management and HR analytics capability building are significant risks constraining adoption.
Recurring risks and constraints reported by multiple included studies; summarized in the review's 'risks and constraints' theme.
Implementation of data-driven HRM faces recurring challenges: data quality, privacy and ethics, algorithmic bias, and deficiencies in skills and organizational readiness.
Commonly reported implementation issues across the 47 reviewed studies; extracted as a central theme in the review's thematic analysis.
Constraints and risks include model risk (overfitting, drift), algorithmic bias, privacy and data-sharing limits, legacy ERP complexity, interoperability challenges, and limited organizational readiness and skills.
Reviewed literature (empirical studies, technical evaluations, and standards) documenting technical and organizational failures, risk incidents, and common barriers to implementation.
Key audit/control weaknesses with respect to prompt fraud include lack of provenance for inputs/prompts and model outputs, inadequate access controls, and missing or ineffective monitoring and anomaly detection for AI outputs.
Qualitative control analysis and adaptation of established auditing principles to GenAI workflows; recommendations based on threat modeling rather than field data.
GenAI outputs can be tailored to mimic corporate styles, templates, and evidence artifacts (e.g., summaries, memos, audit trails), which increases their credibility to auditors, managers, or customers.
Illustrative examples and scenario mapping demonstrating templated output mimicry; no controlled experiments or corpus analysis provided.
Large language models produce fluent, human-like outputs that can mask falsehoods (hallucinations) as facts, making prompt fraud effective.
Well-established LLM behavior cited conceptually and supported in the paper by illustrative examples; no new empirical measurement in this article.
Prompt fraud does not require system intrusion, credential theft, or software exploits; it operates at the reasoning/language layer of large language models and therefore can be executed without technical breaches.
Logical/technical argumentation built from properties of LLMs and illustrative hypothetical attack chains; threat modeling rather than empirical attack logs.
Prompt fraud is a new, distinct fraud modality in which adversaries intentionally craft natural-language prompts (or manipulate prompt inputs) to steer generative AI outputs into producing misleading, fabricated, or compliance-evading artifacts that bypass traditional internal controls.
Conceptual definition presented by the paper based on threat taxonomy and scenario mapping; illustrated with case-style examples. No empirical incident dataset or prevalence statistics provided.
Potential limitations include limited methodological detail on case selection and measurement, possible selection and reporting bias from practitioner-sourced examples, and variable generalizability to small firms or highly regulated industries.
Authors' self-reported limitations in the Methods/Limitations section (qualitative assessment).
China manages the openness–security trade-off through a centralized, developmentalist, techno‑sovereignty approach that privileges coordinated state direction and control.
Qualitative content analysis of national‑level policy texts: 18 Chinese policy documents coded across four analytical dimensions (coordination objectives, institutional actors, governance mechanisms, stakeholder legitimacy).
Automation and LLM-driven orchestration add opacity; errors in instrument control or analysis could propagate quickly, raising liability, insurance, and reproducibility concerns.
Analytical discussion of risks and analogies to automated systems in other domains; no incident-level empirical data from microscopy given.
Ethical and governance issues related to LLM-driven microscopy include accountability, reproducibility, access inequities, data privacy, and concentration of capabilities in large providers.
Policy-oriented synthesis and analogies to governance challenges observed in other AI deployments; no new empirical measurement in microscopy contexts.
Integration of LLMs with microscopes faces challenges including safety and reliability of instrument control, verification of scientific outputs, data provenance, and alignment with experimental constraints.
Analytical discussion based on known reliability and safety issues in automated systems and AI tool use; no empirical incident data from microscopy provided.
Implementing the governed hyperautomation pattern raises upfront costs (governance tooling, monitoring, validation, compliance processes).
Economic and cost-structure discussion in the paper, based on qualitative reasoning and industry experience; no quantified cost estimates or sample-based cost analysis provided.
Federated infrastructures introduce adversarial risks (model/data poisoning, inference attacks on updates) that require robust aggregation, anomaly detection, and other defenses.
Threat modeling and taxonomy of adversarial/privacy threats with mapped mitigations (robust aggregation, anomaly detection, DP). Evidence is conceptual and based on standard threat frameworks; no empirical attack/defense experiments reported at scale.
Delayed and sparse feedback (clicks/conversions) in advertising complicates credit assignment and timely model updates, degrading learning unless specific methods for delayed/sparse signals are used.
Analytical discussion of learning dynamics with delayed/sparse labels; conceptual solutions suggested (credit assignment methods). No large-scale empirical evaluation presented.
Non-IID and heterogeneous data distributions across devices and publishers impair convergence and degrade personalization unless addressed with algorithmic adaptations.
Analytical modeling of convergence under non-IID conditions; threat/robustness discussion; prototype/simulation illustrations. This claim is supported by established literature and the paper's analytic treatment.
Regulatory technology is viewed as a governance arrangement that organizes relations between firms, banks, insurers, logistics actors, buyers, and regulators.
Conceptual framing developed through the interpretive synthesis of multiple literature streams in the paper.
The framework reframes the central question of autonomous software engineering from whether a foundation model can produce a patch to whether the model-harness-environment system can produce a verifiably correct, attributed, and maintainable change.
Conceptual reframing and argument presented in the abstract as a conclusion of the proposed framework and evaluation approach.
We formalize this substrate as 'AI Harness Engineering' and identify eleven component responsibilities: task specification, context selection, tool access, project memory, task state, observability, failure attribution, verification, permissions, entropy auditing, and intervention recording.
Methodological/conceptual contribution described in the paper (abstract) that lists eleven component responsibilities as part of the formalization.
We position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots.
Architectural/framework description in the paper that maps DePIN elements into a vertically integrated stack; conceptual/mapping method without empirical measurement.
We evaluate four mechanisms to enable cooperation: (1) repeating the game for many rounds, (2) reputation systems, (3) third-party mediators to delegate decision making to, and (4) contract agreements for outcome-conditional payments between players.
Description of experimental design / mechanisms evaluated in the study across four social dilemmas; details on implementation and sample sizes not provided in the excerpt.
CoCoGen+ formulates each training round as a weighted potential game in which organizations strategically decide how much synthetic data to generate by balancing learning performance gains against computational costs and competition-caused utility losses.
Theoretical formulation and game-theoretic modeling provided in the paper (analytical derivation); no empirical sample size reported.
Legitimate accountability is axiomatized through four minimal properties: Attributability (responsibility requires causal contribution), Foreseeability Bound (responsibility cannot exceed predictive capacity), Non-Vacuity (at least one agent bears non-trivial responsibility), and Completeness (all responsibility must be fully allocated).
Paper presents an explicit axiomatization listing these four properties as definitions/axioms forming the normative criteria for legitimate accountability.
Collective behaviour is characterised through interaction graphs and joint action spaces.
Paper specifies interaction graphs and joint action spaces as part of the formal model (definitions and formal structure).