Evidence (740 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
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
A distinctive feature of the taxonomy is a dedicated category for self-evaluation: every improvement loop is a claim that some signal can substitute for human judgment.
Authors' taxonomy and conceptual argument emphasizing self-evaluation as a separate category across surveyed works.
The study investigates both perceived and enacted managerial agency.
Stated measurement targets in the abstract (descriptive of dependent variables). No measurement instruments or sample reported in the provided text.
The study focuses on how technological design features, including transparency and override flexibility, interact with governance structures such as accountability and incentive systems.
Stated focus of the study in the abstract (descriptive of independent variables and governance moderators). No empirical details or sample reported in the provided text.
This doctoral research examines how AI-enabled decision systems affect human agency in data-driven organizations.
Stated research scope and aim in the paper (descriptive claim about the study's focus). No sample or results provided in the abstract.
Artificial intelligence is increasingly embedded in organizational decision-making, reshaping how managers exercise discretion and responsibility.
Stated as a background/motivation statement in the paper (literature-driven claim in the abstract). No empirical evidence or sample reported in the provided text.
There exists a six-bit prior for which R_max(μ)/R_0(μ) = 39/31 > 5/4, so no universal 5/4 bound holds.
Constructive counterexample provided in the paper: an explicit six-bit prior is presented and analyzed to compute the ratio. This is a theoretical construction, not empirical data.
If the prior μ is close to the independent product prior with the same marginals in the sense that μ(x) ≥ (1−η) π_μ(x) for every state x, then R_max(μ) ≤ R_0(μ) + η n.
Mathematical derivation/proof in the paper under the stated closeness assumption (formal theorem conditional on parameter η and number of bits n). No empirical/sample data.
For any prior μ, R_max(μ)/R_0(μ) ≤ 3/2.
Mathematical proof (theorem) within the paper's Bayesian persuasion model where the sender is strategic and the receiver guesses bits. The result is presented as a proven upper bound under the model's assumptions (no empirical/sample data).
LLM guidance was associated with increased pupil size variability.
Physiological eye-tracking measure (pupil size variability) reported and compared across conditions in the simulated SAR experiment.
The effectiveness of AI in strategic core functions is contingent upon the human–AI interface.
Stated as a conditional claim in the paper—AI effectiveness depends on the quality of the human–AI interface; no empirical quantification provided in the summary.
GenAI should be understood as more than an educational technology: it affects the development of managerial decision-making, business ethics, and workforce readiness for future managers, entrepreneurs, administrators, policymakers, and business professionals.
Conceptual argument and literature synthesis presented in the review article (no primary empirical sample).
The effect of embeddedness (GenAI being integrated into internal software environments) on employees depends on the presence of organizational authorization.
Reported empirical result from the vignette experiment indicating an interaction effect between embeddedness and organizational authorization (text states 'the effect of embeddedness depends on the presence of organizational authorization').
The research contrasts tool-shaping (AI behavior/prototype) and mind-shaping (user strategy training) pathways and reports differing effects between them.
Paper presents both a tool-shaping experiment (Study 1) and a mind-shaping experiment (Study 2) and discusses comparative findings across these pathways.
Cognitive flexibility is examined as a moderator (boundary condition) of the interventions' effects.
Paper reports including cognitive flexibility as an individual-differences moderator in analyses across the two studies (moderation analysis planned/reported).
Reasoning scaffolds (public tools, playbook, verifier, objectivity policy, red-team) improve calibration and audit discipline, but proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.
Synthesis of experimental results showing B improved calibration/audit metrics while C (with proprietary data) markedly increased coverage and informed decision-quality.
Under capability-superset accounting on the curated gold competitive record, agent A recovers only 0.25, agent B recovers 0.38, while agent C recovers 0.96 (overall).
Capability-superset accounting comparison of fraction of a curated gold competitive record recovered by each agent on the benchmark.
AI reconfigures UET through evaluation reconfiguration: AI partially substitutes human judgment with algorithmic decision logic and thereby shapes how alternatives are evaluated.
Conceptual synthesis from the literature review integrating findings from management and IS studies on algorithmic decision logic and judgment substitution (no primary empirical sample reported).
AI reconfigures upper echelons theory (UET) through cognition reconfiguration: AI mediates information and attention, expanding analytical capacity while introducing new constraints on executive cognition.
Synthesis of management and IS research in a concept-centric literature review; conceptual argument drawing on prior studies about information mediation and attention (no primary empirical sample reported).
Emotion is a strategic action channel rather than a surface style.
Interpretation based on experimental results (GoEmotions prompting and subsequent analyses) demonstrating that adding emotional framing changes negotiation outcomes in systematic ways.
Exploits and proofs of concept remain important, but in defender workflows they primarily prove impact, guide prioritization, and justify remediation rather than serving the same role they did in high-end offensive workflows.
Conceptual argument grounded in collaboration data and public examples (Anthropic Mythos Preview and Mozilla Firefox collaborations cited); no numerical sample size provided in the abstract.
Including narrative explanations with AI predictions may involve tradeoffs for decision-making performance.
Synthesis and conclusion based on the experiment's findings (null effect on accuracy, increased reliance, and exploratory detrimental effects on response time and discrimination).
Consumer decision-making is shifting from linear to nonlinear patterns under intelligent technologies.
Synthesis from the paper's systematic review and content analysis of literature (2010–2025); no sample size or primary empirical study reported in the summary.
Scaling helps but does not solve the accumulated-message effect (Anthropic models: Haiku -0.22 to Opus -0.17; OpenAI models: Nano -0.34 to GPT-5.2 -0.17).
Comparison of effect magnitudes (Cohen's d values) across model families and sizes reported in the experiments.
The accumulated-message effect concentrates on items where the model is genuinely uncertain at baseline (d = -0.34 for high-entropy items, vs d = -0.15 when the baseline is deterministic).
Subset analysis partitioning items by baseline model entropy/uncertainty; reported Cohen's d for high-entropy vs deterministic-baseline items (no separate sample counts reported in the abstract).
Models shift toward the conversation's prevailing polarity (accumulated message effect on LLM judgments, AMEL).
Experimental comparison where identical test items were presented either in isolation or following histories saturated with predominantly positive or negative evaluations, across the full dataset (75,898 API calls to 11 models). Reported effect: d = -0.17, p < 10^-46.
Interpretability, trust calibration, and interface design matter, but they cover only part of what determines whether human-AI combination works.
Authors' argumentative claim based on their analysis and mapping of broader factors; presented as an evaluative conclusion rather than an empirical estimate.
AI creates hybrid cognitive architectures by integrating algorithmic cognition with human cognition, thereby changing how strategic decisions are made.
Theoretical argument drawing on literature in Behavioral Strategy and cognitive theory; conceptual synthesis without reported empirical tests or sample.
Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline.
Illustrated by a hotel-pricing experiment (hidden competitor state) in which a learner achieves plausible revenue per available room while failing to preserve the rate discipline of a rule-based revenue-management competitor; based on experimental results in the paper's two-hotel benchmark.
AI enhances forecasting accuracy only when integrated within institutional decision cycles.
Empirical finding from comparative analysis combining Flexibility Index (including AI integration) with measures of institutional decision cycles; conditional effect reported in results.
There is a fundamental reward-coverage tradeoff: concentrating probability mass on high-reward actions reduces variance but risks missing signal on actions the target policy may take.
Explicit characterization in abstract; claimed theoretical analysis/derivation of the tradeoff between variance reduction and coverage when designing logging policies.
Facilitators shifted select charity-level allocations by up to 5.5 percentage points, directly affecting the final charitable payout.
Analysis of final group allocation outcomes across experimental conditions showing shifts in allocation to specific charities; reported maximum observed shift of 5.5 percentage points attributable to facilitator condition(s). (Study-level sample covering the two experiments; participants organized in groups of three.)
Under open-ended prompts, trust drops to 3-55%, confirming prompt framing as a confound; we report both conditions.
Experimental comparison reported by authors between directed queries and open-ended prompts; observed trust rates under open-ended prompts ranged from 3% to 55% (no explicit per-model sample sizes reported in the summary).
Depending on the used fairness metric, the Pareto frontier may include upper-bound threshold rules, thus preferring individuals with lower success probabilities.
Analytical derivations showing that for certain fairness metrics the set of Pareto-optimal rules includes rules that impose upper-bound thresholds; theoretical examples and arguments in the paper.
Message for AI alignment: smooth scoring-based oversight cannot elicit truthful reports from a strategic agent; sharp thresholds (step functions) are the calibration-preserving design.
Synthesis of the paper's theoretical impossibility and constructive results applied to AI oversight setting (argument plus the step-function constructive escape).
We empirically validate these theoretical observations using both synthetic and real datasets.
Experimental evaluation reported in the paper applying proposed policies and measures to synthetic data and at least one real dataset (details not given in abstract).
These patterns suggest that AI adoption is associated with expected efficiency gains that shape both firms' pricing behaviour and their macroeconomic expectations.
Interpretation based on observed increases in productivity/profitability and different pricing/inflation expectations among adopters vs non-adopters in survey and DID analyses.
Statistical tests confirmed significant performance differences (p ≤ 0.01).
Reported inferential statistics in results: statistical tests comparing strategy performances produced p-values at or below 0.01.
Color-coded reward matrices alter VLM decision patterns.
Experimental condition varying the visual presentation of the IPD payoff matrix (color-coding of rewards) and measuring resulting decision patterns of multiple VLMs in IPD trials. (Reported as part of the experimental setup across models; exact counts not provided in abstract.)
VLM behavior can be influenced by image content depicting behavioral concepts (kindness/helpfulness vs. aggressiveness/selfishness).
Experimental manipulation in the Iterated Prisoner's Dilemma (IPD): VLMs were exposed to images labeled/connoting 'kindness/helpfulness' versus 'aggressiveness/selfishness' and subsequent choices in IPD rounds were recorded across multiple state-of-the-art VLMs. (Paper reports experiments across multiple VLMs; exact sample sizes per model/condition not stated in the abstract.)
The experimental findings are consistent with the paper's theoretical predictions.
Comparison reported in the paper between theoretical model predictions and observed outcomes from the controlled AI-agent trading experiments.
Advanced prompting methods improve accuracy on inconclusive cases but over-correct, withholding decisions even on clear cases.
Empirical comparison of prompting methods reported in paper: advanced prompts increased accuracy on inconclusive (insufficient-information) cases but led to excessive deferral/withholding on clear cases.
AI is increasingly being integrated into both existing and newly emerging digital infrastructures, altering their architecture, functional role, and strategic significance as these systems begin to operate as embedded cognitive infrastructures shaping knowledge production, decision-making, and institutional processes.
Conceptual and descriptive claim presented by the paper (theoretical analysis/literature-informed observation). No empirical sample size or quantitative methods reported in the provided text.
Results reveal divergences between purely simulated and human study datasets.
Abstract reports that findings diverge between simulation experiments and the human-subjects dataset; comparisons drawn across the two datasets (simulation N=2000, human N=290).
Cybersecurity has a moderating effect on audit data analytics.
Synthesis statement in the review summarizing included studies that report cybersecurity influences the effectiveness/usability of audit data analytics.
A within-subject human study with 20 players and 600 games shows that our interventions significantly improve performance for low- and mid-skill players while matching expert-engine interventions for high-skill players.
Within-subject human experiment reported in the paper: N = 20 players, 600 games total; comparisons of performance under the proposed interventions versus expert-engine interventions.
Algorithmic credit scoring is accomplished through the ongoing work of alignment that stabilizes risk under conditions of persistent uncertainty, taking epistemic, modeling, and contextual forms.
The paper's theoretical argument grounded in nine-month ethnographic observations and analysis of how practitioners and institutions engage in alignment work across epistemic, modeling, and contextual dimensions.
Practitioners formulate risk through multiple interpretations.
Ethnographic evidence from interviews and observations indicating that risk is characterized differently across actors (technical, legal, business interpretations).
Subjectivity persisted in AI-powered recruitment decisions; human judgment remained an important factor.
Theme 2 (subjectivity in AI-powered recruitment) from interviews indicating retained human subjectivity and judgement in recruitment processes (n = 22).
Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users' emotional trust.
Comparison between concurrent and sequential AI-assisted decision-making paradigms in the RCT (N=120); authors report concurrent < sequential for immediate task performance, but concurrent > sequential for emotional trust.
Analysis uncovers dramatic asymmetries: inhibition 17.6% vs. preference 75.0%.
Paper reports specific aggregated percentages for two types of implicit effects (inhibition and preference) observed in their analysis; methodology context implies these are results from the benchmark evaluation (300 items / 17 models).