Evidence (7560 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.

Human-AI Collaboration

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

Human Ai Collab Remove filter

The AR-MLLM prompt/design framework is adaptable to other industrial machine-operation scenarios.

Authors state generalizability as an argument based on the architecture and iterative prompt design; the empirical evaluation in the paper is limited to the CMM case study (no cross-domain experiments reported in the provided summary).

speculative positive Augmented Reality-Based Training System Using Multimodal Lan... Adaptability/generalizability to other machine-operation domains (not empiricall...

The Reversal Register is an auditable institutional artifact that records for each decision the prevailing authority state, trigger conditions causing transitions, and justificatory explanations, thereby supporting auditability and research.

Design specification and instrumentation proposal in the paper; description of required metadata fields and intended uses. No implemented dataset presented.

medium-high positive Human–AI Handovers: A Dynamic Authority Reversal Framework f... auditability_score; presence_of_register_entries; completeness_of_justificatory_...

Firms that build effective orchestration layers and integrate AI across pipelines may capture outsized gains, increasing winner-take-all dynamics and concentration.

Authors' argument extrapolated from observed coordination benefits/frictions at Netlight and theory about returns to scale in platformized toolchains; no empirical market concentration analysis provided.

speculative positive Rethinking How IT Professionals Build IT Products with Artif... firm-level returns and market concentration from AI orchestration capabilities

Policy and firm responses should emphasize human-in-the-loop governance, training in evaluative/domain skills, data stewardship, and regulatory attention to IP, liability, competition, and robustness standards.

Normative recommendations drawn from the review's synthesis of empirical benefits and limitations; based on identified failure modes (bias, hallucination, variable quality) and economic risks (concentration, mismeasurement).

speculative positive ChatGPT as an Innovative Tool for Idea Generation and Proble... effectiveness of governance/training/regulation in mitigating harms and enhancin...

Policy and regulation should emphasize transparency, auditability, and model-validation standards in finance to reduce systemic risks from misplaced trust or opaque algorithms.

Authors' normative recommendation based on empirical identification of risks (misplaced trust, overreliance) from survey/interview/operational data; recommendation is prescriptive and not an empirical test within the study.

speculative positive Human-AI Synergy in Financial Decision-Making: Exploring Tru... policy/regulatory emphasis (transparency/auditability); reduction in systemic ri...

Public goods investments—digital infrastructure, interoperable local data ecosystems, and multilingual language technologies—are prerequisites for inclusive economic benefits from AI.

Conceptual and policy literature review arguing for infrastructure and public data ecosystems; paper does not provide original infrastructure impact analysis.

medium-high positive Towards Responsible Artificial Intelligence Adoption: Emergi... infrastructure coverage (broadband, cloud), interoperability standards/adoption,...

A culturally grounded responsible‑AI governance framework based on Afro‑communitarianism (Ubuntu) and stakeholder theory—emphasizing collective well‑being and participatory governance—can help align AI deployment with inclusive and sustainable economic outcomes.

Theoretical integration and framework development based on normative literature in ethics, Afro‑communitarian thought, and stakeholder governance; framework is conceptual and not empirically validated in this paper.

low-medium positive Towards Responsible Artificial Intelligence Adoption: Emergi... governance inclusivity, alignment of AI outcomes with communal values, perceived...

Firms with large, integrated datasets and standardized processes can gain disproportionate returns, creating potential scale economies and winner-take-most dynamics.

Resource-based theoretical interpretation and illustrative patterns in the reviewed literature; the paper notes empirical evidence is limited and calls for further study.

speculative positive Integrating Artificial Intelligence and Enterprise Resource ... scale-dependent returns (e.g., differential ROI by firm data scale/integration l...

Explainable EEG tools can shift clinician workflows by enabling faster decision-making and reducing the requirement for specialized interpretation, with implications for training, staffing, and productivity.

Projected operational impacts discussed as implications of improved explainability; no longitudinal workflow study provided in the reviewed literature.

speculative positive Explainable Artificial Intelligence (XAI) for EEG Analysis: ... clinician workflow efficiency, training/staffing needs, productivity

Policy and managerial implication suggested: investing in short, targeted onboarding/training for GenAI tools (rather than only providing access) may deliver measurable performance gains and increase voluntary adoption.

Authors derive this implication from the randomized trial results showing increased adoption and improved scores with brief training (n = 164); this is an extrapolation from the trial findings.

speculative positive Training for Technology: Adoption and Productive Use of Gene... Organizational adoption and productivity (extrapolated from student trial outcom...

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