Evidence (176 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 |
The cooperative effects of the prosocial AI interventions were short-lived, fading after the first few rounds.
Temporal analysis of contributions over rounds in the iterated game showing decay of the prosocial AI effect after the initial rounds (reported in the experiment with N = 1,283).
Personality effects depend critically on task structure.
Authors compared the impact of personality manipulation across three distinct task domains (structured coding, open-ended research collaboration, competitive bargaining) and report differing outcomes by domain. Abstract does not provide numeric sample sizes or statistical details.
Du et al. (2026) find that information-based team faultlines can enhance proactive behavior via deep information processing, while AI adoption moderates and mitigates the negative effects of social-based faultlines on team cooperation.
Information-processing theoretical framing and empirical analysis reported in the paper (study type and sample size not specified in the excerpt).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
There is a suggestive non-linear relationship between embodiment and team performance.
Analysis reported in the paper indicating a non-linear (not strictly monotonic) association between degree of agent embodiment (Box, Avatar, humanoid) and measured team performance; described as 'suggestive' in the abstract, without quantified functional form or statistics included there.
Artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse.
Observed performance outcomes across mixed human–AI teams in the escape room experiment, showing high between-team variability; exact sample size and statistical details not provided in the abstract.
cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
Interpretive/concluding claim based on experimental results (timing-dependent failure modes, Oracle gating, Hybrid Fusion effects) reported in the study.
AI timing dictates the mechanism of team failure: high-speed AI interventions risk inducing reflexive blind compliance while delayed interventions can induce ambiguous cognitive conflict.
Synthesis claim derived from experimental contrasts between Fast/Less-Accurate and Slow/Accurate AI conditions and observed human/team behaviors (blind compliance vs. delayed conflict).
GenAI enables small teams to expand capacity while creating new dependencies and coordination logics.
Empirical finding from 17 interviews indicating both expanded capacity and emergent dependencies/coordination needs.
Across studies, causal modeling reveals that cognitive alignment systematically drives attentional coordination in successful collaboration, while mismatches between effort and attention characterize unproductive regulation.
Synthesis of causal inference results from the three studies using time-series measures (JME, JVA) and episode-based analyses across the pooled dataset (182 dyads total).
Augmentation is bounded rather than linear (i.e., human-AI augmentation shows diminishing or negative returns past a balanced zone).
Synthesis of interview themes across 34 cases producing the bounded-augmentation / curvilinear conceptualization.
Mediators such as trust, cohesion and accountability are reshaped when AI-generated contributions enter collaboration.
Thematic evidence from interviews indicating changes in trust, cohesion and accountability dynamics associated with the introduction of AI outputs into team collaboration.
Social (leadership engagement, trust, ownership, mediation and alignment) and technical (automation, creation, reliability, distraction and integration) subsystems combine to enable or erode team effectiveness, summarized in an e-leadership–AI orientation matrix.
Analytic synthesis from thematic coding (Gioia-informed) of interview data producing a conceptual matrix mapping social and technical factors to outcomes.
Analysis identifies a curvilinear pattern of bounded augmentation, where effectiveness peaks in a zone of balanced use but declines under under-use and over-reliance.
Thematic (Gioia-informed) analysis of 34 semi-structured interviews with project managers across five UK industries; pattern emerges from cross-case coding and synthesis.
We identify significant differences between human and AI negotiation behaviors, finding that humans favor lower-complexity deals and are significantly less reliable partners compared to LM-based agents.
Results from the user study comparing human vs LM-based agent negotiation behavior (statements in the results section).
The authors identify ten evaluation practices that teams use, ranging from lightweight interpretive checks to formal organizational processes (examples: qualitative user reviews, red-team testing, A/B experiments, telemetry/log analysis, structured annotation, governance/meta-evaluation).
Thematic coding of 19 interview transcripts produced a taxonomy enumerating ten practices (paper reports the taxonomy as an outcome).
Teamwork partner type moderates the effect of service empathy on collaboration proficiency (i.e., the impact of service empathy on proficiency differs by human vs AI partner).
Reported interaction/moderated-mediation analyses from the online experiment (n = 861) indicating a significant partner-type × service-empathy interaction predicting collaboration proficiency.
Employees' emotional state significantly moderates the relationship between partner type (human vs AI) and collaboration proficiency.
Moderation analyses reported from the same online experimental dataset (n = 861), testing interaction terms between partner type and measured employee emotion on collaboration proficiency; authors report a significant moderating effect.
AI excels at hypothesis generation but cannot replace scientific reasoning and experimental validation; human expertise remains essential.
Argument and case examples in the paper showing AI-generated hypotheses requiring human-led experimental design, interpretation, and validation.
AI use can reduce visibility of real skill differences among employees.
Reported findings from performance management and knowledge-work studies indicating that AI-mediated outputs can obscure underlying employee skill variation.
Agent-authored pull requests are discussed less than human-authored ones.
Observational analysis of public GitHub activity reported in the paper (no sample size reported in abstract); comparison of discussion volume/length for agent- vs human-authored PRs.
Aligning AI systems with human teams remains a major challenge to realizing AI's full potential in organizations.
Authors' statement in abstract framing the motivation for the study; supported by literature cited in full paper (abstract asserts this as a core challenge).
Computational theorising, synthetic task simulations, real LLM agent traces, and robustness analyses show that human-imitation forms often underperform when they add lossy handoffs, correlated deliberation, and verification burdens.
Empirical and simulation-based methods listed in the paper (computational theorising, synthetic task simulations, analysis of real LLM agent traces, robustness checks). The excerpt does not report sample sizes, numeric effect sizes, or statistical tests.
There is an asymmetry between prosocial and antisocial persuasion: antisocial (selfish) persuasion produces larger and more persistent reductions in cooperation than prosocial persuasion produces increases.
Direct experimental comparison of prosocial versus antisocial AI persuasion treatments in the iterated Collective Risk Game showing differential magnitudes and temporal persistence of effects (reported results from N = 1,283).
The larger and more persistent negative effects of antisocial AI persuasion were particularly pronounced for personalized interventions.
Subgroup or interaction analysis in the experiment indicating that personalization (targeting by Social Value Orientation) amplified the persistence and magnitude of antisocial framing effects (reported within the N = 1,283 sample).
When AI treatments were reconfigured to promote selfish behavior through exculpatory framing, the negative effects on contributions and group success were larger and substantially more persistent.
Experimental comparison between prosocial and antisocial (exculpatory/selfish) AI treatments in the iterated Collective Risk Game showing larger and longer-lasting reductions in contributions and lower group success rates under antisocial framing (reported across N = 1,283).
In open-ended collaboration and bargaining, the same manipulation substantially degrades performance.
Experimental manipulation of agreeableness in LLMs on open-ended research collaboration and competitive bargaining tasks; authors report substantial performance degradation in these domains. Abstract lacks numeric metrics, sample sizes, and statistical significance details.
Under individual selection, self-interested prompts dominate, causing populations to collapse into collective defection.
Simulation experiments with individual-level selection/transmission showing emergence and dominance of self-interested prompts and subsequent decline into collective defection.
As frontier training shifts toward individual rewards for verifiable tasks (e.g., mathematics and coding), this outcome-based focus may further undermine cooperation in multi-agent settings.
Argumentative/prognostic claim in the paper's motivation; not an empirical result from the study but framed as a risk informed by the literature and authors' reasoning.
Current approaches to instill prosociality in LLM agents often rely on humans specifying desired behaviors at the individual level, which does not guarantee cooperation within LLM populations.
Background statement in paper; conceptual critique of human-specified, individual-level reward/behavior specification as commonly used in LLM alignment and fine-tuning literature (no new empirical test reported in this study).
Adding relevant collaborators can lower performance when teams lack structure to coordinate their contributions.
Empirical comparisons across experimental sessions in the Collaborative Gym / DiscoveryBench setup; result reported across the study (1,482 sessions).
Pure behavioural teams (N=8) failed to scale beyond 74.1%.
Reported team performance metric for 'pure behavioural' teams with sample size N=8; maximum reported performance 74.1%.
Reactive approaches paired with automation or creation produced breakdowns (reduced effectiveness).
Thematic evidence from interviewees describing instances where reactive leadership combined with high automation-or-creation use led to coordination or accountability breakdowns across the 34 cases.
Studies highlight concerns around cognitive offloading and reduced team collaboration when using LLM-assistants.
Synthesis of reported negative effects in included studies (themes extracted by the authors).
This condition of authorship uncertainty reshapes how teams attribute ideas, negotiate accountability, and coordinate collective reasoning.
Theoretical claim based on conceptual analysis in the paper; no empirical method or sample described in the abstract.
OpenAI o3 achieves only 17% of optimal collective performance.
Experimental measurement of collective performance for OpenAI o3 in the paper's multi-agent setup (value reported in abstract; no sample size provided there).
Practitioners identified specific functional deficiencies in AI: inability to maintain sustained partnerships.
Theme from semi-structured interviews with 10 practitioners; cited as an example of the functional gap.
Practitioners currently view AI models as intellectual teammates rather than social partners and expect fewer SEI attributes from them than from human teammates.
Qualitative findings from semi-structured interviews with 10 software practitioners reported in the study.
Current AI systems lack SEI capabilities that humans bring to teamwork, creating a potential gap in collaborative dynamics.
Framed as background/context in the paper; asserted rather than empirically tested in this study.
For organizations of n humans with AI agents, the optimal team size decreases with agent capability.
Derived implication from the stylized model's analysis of multi-human organizations interacting with AI agents.
Developers and experts still lack a shared view, resulting in repeated coordination, clarification rounds, and error-prone handoffs.
Observational/qualitative claim in paper describing current MSD practice (no numeric sample reported).
Cooperation with the AI plateaus and never reaches the near-complete cooperation levels observed in human–human interactions.
Time-series/trajectory analysis of cooperation rates in the lab human–AI experiment (n = 126) compared to the human–human benchmark (n = 108); reported convergence/end-state cooperation levels show AI condition asymptotes below the human–human condition.
Across studies, performance was the most frequently examined aspect, followed by trust, explainability and transparency, decision-making, and team processes.
Synthesis and frequency coding of outcomes/measured constructs across the 104 included empirical studies.
Production deployments are no longer one human supervising one model; they are multi-human, multi-agent collaborations that cross teams, time zones, and trust boundaries.
Stated as a general characterization of modern production deployments; no quantitative data or case counts provided in the excerpt.
Neither survey nor transcript-based measures of participation equity improved under LLM facilitation (an "illusion of inclusion").
Quantitative survey measures and transcript-based analyses of participation equity (e.g., measures of turn-taking, speaking/typing share) showed no improvement in equity metrics for facilitated conditions compared to controls across the experiments.
Capability does not predict cooperation.
Comparative experimental results reported in the paper showing different models with different capability levels achieving substantially different collective cooperation outcomes (specifically comparing OpenAI o3 and o3-mini).
Allowing repeated pre-play communication (chat before every round) has no detectable effect on cooperation rates when the partner is an AI.
Between-subjects manipulation within the human–AI experiment comparing chat-before-first-round vs chat-before-every-round treatments (human–AI n = 126 total); statistical comparison of cooperation rates across the two chat-frequency treatments showed no detectable difference.
Initial cooperation rates against the AI (GPT-5.2) are high and comparable to initial cooperation in human–human pairs.
Laboratory experiment with human subjects playing an indefinitely repeated Prisoner’s Dilemma against an AI chatbot (GPT-5.2); human–AI sample n = 126; human–human benchmark taken from Dvorak & Fehrler (2024) with n = 108; comparison of initial-round / early-round cooperation rates across conditions.
Four autonomous agents were benchmarked on the same fresh CTF challenge set alongside human teams.
Benchmarking experiment described in the study: four autonomous AI agents evaluated on the identical fresh challenge set used in the live onsite CTF.
Teamwork partner type (human vs AI) has no direct, significant effect on collaboration proficiency for temporary virtual tasks.
Online experiment with employees in the online-retail industry (n = 861). Hypothesis testing showed no significant main effect of partner type on the outcome variable 'collaboration proficiency' in the reported analyses.