Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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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.
Generated code can introduce security vulnerabilities.
Security analyses and code audits documenting examples where LLM-generated code contains known vulnerability patterns; incident-oriented case studies and controlled experiments assessing vulnerability incidence.
LLMs can produce plausible-looking but incorrect or insecure code (so-called 'hallucinations').
Benchmarks and controlled tests demonstrating incorrect outputs; security analyses and replicated examples showing erroneous or insecure snippets produced by LLMs across multiple models and prompts.
The technical feasibility of robust token verification and resistance to spoofing needs demonstration; it is not yet proven.
Authors explicitly acknowledge this limitation in the paper; no prototypes or red-team results are presented.
Risks: dependence on LLM behavior means hallucinations, bias, or misaligned reasoning can propagate into simulated outcomes; Chain-of-Thought reasoning may be hard to fully verify, posing interpretability/auditability challenges.
Paper's cautions section listing potential failure modes and ethical/interpretability risks; these are identified risks rather than quantified failures observed in experiments.
Key failure modes for AI in drug R&D include overfitting, poor generalizability, dataset bias, insufficient external validation, and misalignment with evolving regulatory expectations.
Synthesis of literature and case reports in the narrative review describing observed failures and risks across projects (qualitative evidence).
Absent rigorous controls (validation, applicability-domain reporting, attention to dataset bias), AI models risk overfitting, producing inequitable outcomes and regulatory friction that can undermine economic benefits.
Theoretical arguments plus case reports and literature cited in the review documenting instances and mechanisms of overfitting, dataset bias, and regulatory challenges; narrative summary rather than systematic quantification.
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.
Adaptive RL-driven campaigns complicate attribution and causal inference, so rigorous experimental designs (multi-armed trials, off-policy evaluation) are required for valid measurement.
Methodological claim in the implications section; supported by discussion of policy adaptivity and the need for specific evaluation techniques. No empirical demonstration provided.
The system raises privacy, fairness, and safety risks including data leakage, demographic bias in generated content, manipulative targeting, and potential regulatory non-compliance.
Risk assessment and red-team / audit practices described; paper cites known classes of ML deployment risks and recommends logs/audits. This is a conceptual identification rather than a quantified empirical finding.
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.
Factual errors and 'hallucinations' create misinformation risks and can produce costly service failures.
Model evaluation studies, incident case reports from deployments, and academic/industry analyses documenting hallucination rates and concrete failure examples.
The study population was restricted to CHI conference papers that had publicly shared study data and analysis code (a self-selected subset), which introduces a self-selection bias that may overestimate reproducibility rates for the broader set of CHI papers.
Authors' stated sampling strategy and limitations noted in the paper (sample restricted to artifact-sharing papers and potential overestimation of reproducibility).
Ethical, privacy, and legal restrictions sometimes limit the ability to share data and thereby hamper reproducibility.
Authors' observations from reproduction work and survey/interview responses indicating that some datasets could not be shared for legal/ethical reasons.
High linguistic diversity in Africa makes building and evaluating multilingual language technologies more difficult and is a barrier to inclusive AI.
Synthesis of technical literature on NLP and multilingual model development and policy/NGO reports highlighting missing language resources; no original model evaluation reported.
Structural constraints—limited digital infrastructure, scarce and skewed data, and high linguistic diversity—complicate AI development, deployment and evaluation in African contexts.
Desk review of infrastructure and data availability reports and scholarly literature demonstrating gaps and their effects; no new measurement in this paper.
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.
Rapid skill obsolescence in AI necessitates frequent curriculum updates and responsive governance.
Identified as a risk: the paper notes AI skill change rates and recommends frequent updates and governance mechanisms. This aligns with general domain knowledge; the paper does not provide empirical measurement of obsolescence rates.
Aligning multiple standards is complex, posing a disadvantage and implementation risk.
Stated explicitly in Disadvantages/Risks: complexity of aligning multiple standards is listed. This is a reasoned observation in the paper rather than empirically demonstrated.
Implementing this framework requires significant resources and continuous updating.
Stated explicitly under Main Finding and Disadvantages/Risks; paper lists cost/time metrics to track (cost-per-curriculum, time-to-update) and highlights resource intensity. Support is descriptive/analytic rather than empirical.
Algorithmic bias, unequal digital financial literacy, caregiving time constraints, and limited access to personalized solutions can sustain or reproduce gender investment gaps if not addressed.
Synthesis of literature on barriers to financial inclusion and AI fairness concerns, plus platform report observations (review of empirical and conceptual studies; not a single empirical test).
Women statistically exhibit greater risk aversion in some settings compared with men.
Summary of empirical survey and experimental studies on gender differences in risk attitudes discussed in the review (multiple cross‑sectional and lab/field experiments referenced).
The digital divide (lack of reliable electricity and connectivity) constrains adoption of MIS and AI, creating geographic and regional inequities in who benefits from the framework.
Infrastructure constraint argument presented in the paper; no quantified coverage maps or population-level access statistics included.
AI-driven equivalency systems carry risks including algorithmic bias, opaque decisions without explainability, and potential reinforcement of inequities when training data under-represents some regions/institutions.
Risk assessment drawing on established AI ethics literature; no empirical bias audit from the proposed system is provided.
The major disadvantage of an MIS is dependency on reliable electricity and internet, creating systemic vulnerability due to the digital divide.
Paper notes infrastructure dependency as a constraint; assertion grounded in common infrastructural realities but no measured connectivity or outage statistics from DRC/SA are provided.
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).
Prompt fraud exploits the natural-language interface of large language models (LLMs) to produce outputs that appear authoritative (reports, audit trails, explanations) without system intrusion, credential theft, or software exploitation.
Definition and threat-model description using conceptual examples and case vignettes; literature/regulatory review to position the threat relative to traditional fraud vectors.
Data privacy and cross-border compliance issues arise from using cloud and SECaaS, complicating legal compliance for firms.
Regulatory analyses and compliance reports; documented examples in case studies and industry guidance on cross-border data flows.
The cloud shared responsibility model creates potential ambiguities in liability between providers and customers.
Regulatory guidance, legal analyses, and documented post-incident case studies showing confusion over responsibilities.
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).
Antibiotic use in humans and animals, along with environmental antibiotic residues, generates converging selection pressures that drive AMR relevant to children.
Well-established ecological and microbiological literature summarized in the review showing cross-sector selection pressures; narrative integration rather than new empirical analysis.
Child behaviors (hand-to-mouth activity, play, outdoor exposure) increase contact with environmental and animal reservoirs and therefore exposure risk.
Behavioral and exposure studies synthesized narratively; observational evidence from exposure assessments and pediatric environmental health studies cited in review (no meta-analysis).
Developmental windows imply early-life exposures can have long-term consequences for health and human capital.
Developmental and epidemiologic literature integrated in the review; narrative citations of studies linking early exposures to later health and cognitive outcomes (no single longitudinal dataset presented).
Physiological and immunological immaturity (including neonatal risks) increases children's susceptibility to infectious disease and related harms.
Established biological and clinical literature synthesized in the review; references to neonatal clinical risks and immunological immaturity across pediatric literature (no pooled effect sizes reported).
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.