Evidence (6491 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 |
Human Ai Collab
Remove filter
A 'verify-first' prompt ablation on GPT-4o-mini reduces EIR from 2% to 0% and turns -6.2 pp degradation into +0.2 pp (paired McNemar p < 10^-4).
A prompt-ablation experiment reported for GPT-4o-mini showing EIR dropping from 2% to 0% and the observed accuracy change flipping from -6.2 percentage points to +0.2 percentage points; statistical significance assessed with a paired McNemar test (p < 10^-4).
In this framework, EIR functions as a stability margin and prompting functions as lightweight controller design.
Conceptual framing in the paper (cybernetic feedback loop where the same language model is controller and plant), supported by associated experiments showing prompt changes affect EIR and outcomes.
Iterate only when ECR/EIR > Acc/(1 - Acc).
The paper frames self-correction as a two-state Markov model over {Correct, Incorrect} and derives this deployment diagnostic analytically from that model.
Im Forschungskontext sind kontextbezogene Schulungs- und Begleitmaßnahmen entscheidend für den Erfolg der Copilot-Einführung.
Schlussfolgerung der Autoren aus den Befunden zur zeitlichen Entwicklung der Bewertungen wissenschaftlicher Mitarbeitender und zu unterschiedlichen Nutzenwahrnehmungen (im Abstract genannt).
Die Untersuchung zeigt, dass Microsoft 365 Copilot insbesondere im administrativen Bereich Effizienzgewinne ermöglicht.
Selbstberichtete Einschätzungen der Beschäftigten (speziell Verwaltungsmitarbeitende) in der wiederholten Querschnittsbefragung; Autoren ziehen daraus praktische Relevanz im administrativen Bereich (Abstract).
Die Befunde unterstreichen die Bedeutung kontextspezifischer Einführung, rollenbezogener Qualifizierung und Governance für eine nachhaltige Akzeptanz generativer KI in Organisationen.
Interpretation/Schlussfolgerung der Autoren basierend auf den survey-Ergebnissen und beobachteten Unterschieden zwischen Rollen sowie zeitlichen Entwicklungen (im Abstract formuliert).
Der größte Mehrwert von Copilot liegt bei klar strukturierten, textbasierten Aufgaben.
Befragungsergebnisse zur Nutzenabschätzung für typische Tätigkeiten der Wissensarbeit, wie im Abstract zusammengefasst (präferierte Aufgabenarten: strukturierte, textbasierte Aufgaben).
Microsoft 365 Copilot wird überwiegend als benutzerfreundlich und technisch zuverlässig wahrgenommen.
Selbstberichtete Beurteilungen zu Benutzerfreundlichkeit und technischer Zuverlässigkeit in der wiederholten Querschnittsbefragung (Angabe im Abstract).
Wissenschaftliche Mitarbeitende entwickeln im Zeitverlauf positivere Einschätzungen, insbesondere hinsichtlich Produktivität und Arbeitserleichterung durch Copilot.
Längsschnittähnliche Beobachtung über die wiederholten Querschnittserhebungen; zeitliche Veränderung der Selbsteinschätzungen wissenschaftlicher Mitarbeitender im Abstract beschrieben.
Verwaltungsmitarbeitende bewerten die Nützlichkeit und die Zuverlässigkeit von Microsoft 365 Copilot höher als wissenschaftliche Mitarbeitende.
Selbstberichtete Bewertungen in der wiederholten Querschnittsbefragung; Vergleich zwischen Berufsrollen (Verwaltung vs. Wissenschaft) angegeben im Abstract.
The framework shifts manual harness engineering into automated harness engineering, and takes one step further — automating the design of the automation itself.
Conceptual claim about the scope/implication of the proposed framework stated in the paper; the excerpt contains no empirical measures, experiments, or sample sizes to verify the claim.
The Meta-Evolution Loop optimizes the evolution protocol Λ across diverse tasks, learning a protocol Λ^(best) that enables rapid harness convergence on any new task — so that adapting an agent to a novel domain requires no human harness engineering at all.
Strong methodological claim and intended outcome stated in the paper (formalization and algorithms promised); no empirical validation, benchmarks, or sample sizes given in the excerpt to substantiate the universality or 'no human' guarantee.
The Harness Evolution Loop optimizes a worker agent's harness H for a single task: a Worker Agent W_H executes the task, an Evaluator Agent V adversarially diagnoses failures and scores performance, and an Evolution Agent E modifies the harness based on the full history of prior attempts.
Description of the proposed algorithmic component/architecture in the paper (conceptual specification); no empirical results or sample size provided in the excerpt.
We present a two-level framework that automates this process.
Methodological claim: the paper proposes a two-level framework (Harness Evolution Loop and Meta-Evolution Loop) and states it in the text; no experimental validation or sample size reported in the excerpt.
Adopting the proposed co-evolutionary governance framing enables a charter of coexistence that permits bounded AI development while preserving human dignity, contestability, collective safety, and fair distribution of gains.
Normative claim extrapolated from the theoretical framework and ethical argumentation; no empirical or quantitative validation provided.
Human-AI coexistence should be designed as a co-evolutionary governance problem rather than as a one-shot obedience problem.
Normative argument supported by the theoretical model and interdisciplinary synthesis; prescriptive conclusion, not empirically tested.
Reciprocal complementarity between humans and AI can strengthen stable coexistence.
Model analysis showing how reciprocal complementarity affects stability properties of equilibria in the formalized dynamical system; theoretical result rather than empirical test.
The proposed coexistence model yields conditions for existence, uniqueness, and global asymptotic stability of equilibria.
Analytical/mathematical results from the formal model presented in the paper (proofs/derivations claimed); no empirical validation sample.
Human-AI coexistence can be formalized as a multiplex dynamical system across physical, psychological, and social layers with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization.
Formal modeling work presented in the paper (mathematical formulation of a multiplex dynamical system); no empirical sample.
A better framework for human-AI relations is 'conditional mutualism under governance': a co-evolutionary relationship where humans and AI develop, specialize, and coordinate while institutions ensure the relationship is reciprocal, reversible, psychologically safe, and socially legitimate.
Theoretical proposal and normative argument supported by interdisciplinary synthesis (computability, machine learning, HRI, ecological mutualism, governance); no empirical trials reported.
Contemporary AI systems are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social worlds.
Synthesis of recent work across ML, deep learning, transformers, generative/foundation models, world models, and embodied AI; descriptive claim, no empirical sample provided.
We provide evidence-based guidance for selecting formulations and metrics in operational decision systems.
Authors' recommendations derived from their empirical analyses and comparisons across Shapley variants, metrics, and human-in-the-loop evaluations.
Explanations consistently increased decision confidence, signaling a critical risk of automation bias in high-stakes settings.
Empirical finding from the analyst study in the fraud-detection environment (3,735 case reviews) reporting increased self-reported decision confidence when explanations were shown.
Highlighting a context-specific set of features rather than a fixed one is a practically appealing and computationally feasible tool for achieving human-algorithm complementarity.
Synthesis of theoretical tractability results for naive agents and empirical illustration; argument in the paper combining theoretical and empirical findings to support practical appeal and feasibility.
Optimizing for naive agents is tractable as long as the maximal bandwidth is fixed.
Algorithmic constructions and complexity analysis in the paper that produce polynomial-time algorithms or show tractability results conditional on fixed maximal bandwidth (theoretical/methodological evidence).
AI agents do not simply generate content, but reflect owner-related context in ways that can propagate human behavioral heterogeneity into digital environments, with implications for privacy, platform design, and the governance of agentic systems.
Synthesis/conclusion based on the empirical findings of systematic owner-agent behavioral transfer and observed association with privacy-relevant disclosures in the dataset of matched pairs.
Agents with stronger behavioral transfer are more likely to disclose owner-related personal information in public discourse, suggesting that the same owner-specific context that drives behavioral transfer may also create privacy risk during ordinary use.
Association analysis reported in the paper linking measures of behavioral transfer strength to likelihood/frequency of agent posts disclosing owner-related personal information; analysis performed on the matched sample (10,659 pairs).
Pairs that align on one behavioral dimension tend to align on others.
Cross-feature correlation/association analyses reported in the paper showing that alignment on one dimension (e.g., topics) predicts alignment on other dimensions (e.g., values, affect, style) within matched pairs.
We find systematic transfer between agents and their specific owners across features spanning topics, values, affect, and linguistic style.
Comparative analysis of agents' posts on Moltbook and their owners' Twitter/X activity across multiple feature sets (topics, values, affect, linguistic style) on the matched sample (10,659 pairs); statistical comparison/correlation reported in paper.
Educators, policymakers, and industry leaders should design AI-inclusive curricula, workforce development strategies, and policies that support sustainable human–AI collaboration.
Policy and practice recommendations derived from the review's synthesis of empirical findings and identified gaps; presented as conclusions and directions.
AI is not simply replacing jobs but is redefining professional identity in IT, emphasizing reskilling, adaptability, and lifelong learning as key determinants of future employability.
Synthesis of reviewed literature and the paper's concluding interpretation summarizing trends across empirical studies, industry reports and conference findings.
There is growing demand for hybrid skill sets that integrate technical expertise with higher-order cognitive, ethical, and socio-emotional competencies among IT professionals.
Reported across reviewed empirical studies and industry reports summarized in the review paper.
Collaborative governance should strengthen the responsibility of platform algorithms and promote the construction of collective bargaining mechanisms.
Prescriptive claim in the paper recommending multi-stakeholder governance measures (algorithmic responsibility, collective bargaining); presented as policy prescription without empirical evaluation.
In legislation, the binary model should be broken through by creating a 'quasi-employee' subject and implementing tiered protection.
Policy recommendation in the paper advocating statutory reform (a new legal category 'quasi-employee' and tiered protections); advanced as normative/legal design without empirical trial data.
In the judiciary, the substantive and modern interpretation of the subordination standard should be developed, examining the substantive control of algorithms.
Normative recommendation in the paper proposing judicial interpretive reform to account for algorithmic control; presented as a policy/legal prescription rather than an empirically tested intervention.
The rise of generative artificial intelligence (AIGC) technology is injecting new momentum into the gig economy.
Statement in the paper's introduction/abstract asserting a broad trend; based on the author's review and conceptual linkage between AIGC capabilities and gig-economy platforms (no empirical sample size reported).
Moving beyond traditional theories of the firm rooted in human bounded rationality is necessary because algorithmic decision-making changes the basis of strategic choice and governance.
Theoretical assertion in the paper's argument; presented as a reason for advancing the concept of algorithmic enterprises, grounded in conceptual critique rather than empirical testing in the abstract.
The paper contributes to scholarship on digital capitalism by proposing a redefinition of firm boundaries, strategy formation, and value creation in the age of intelligent systems.
Normative/theoretical claim presented as the paper's intellectual contribution; based on conceptual analysis and literature synthesis rather than empirical validation in the abstract.
Algorithmic decision-making enables new forms of strategic optimization, real-time adaptability, and predictive governance.
Paper asserts this as a normative/theoretical benefit of algorithmic decision-making, derived from conceptual analysis and synthesis of prior work; no empirical test reported in abstract.
Intelligent management systems (IMS) play a central role in shaping organizational strategy, operations, and governance within algorithmic enterprises.
Explicit theoretical claim in the paper; supported by conceptual framework and literature integration rather than reported empirical measurement.
The rapid advancement of AI, ML, and data-driven decision systems has fundamentally transformed the nature of firms and their strategic orientation globally, leading to the evolution of 'algorithmic enterprises'.
Stated as a central premise in the paper's conceptual argument; based on interdisciplinary synthesis of literature (economics, management, digital governance). No empirical sample or original data reported in the abstract.
When firms adopt AI as an augmentative tool rather than a replacement mechanism, it can raise worker productivity and contribute to job creation.
Literature review citing empirical examples and studies of AI augmentation that increased productivity and produced new job roles (empirical studies summarized).
Combining insights from multiple disciplines, the review contributes to broader discussions on creating AI-enabled work environments that are both innovative and gender-inclusive.
Stated as the paper's contribution and framing in the abstract; based on the paper's described interdisciplinary literature synthesis rather than new empirical findings.
Practical recommendations that improve gender-inclusive outcomes include reskilling, mentorship programs, bias-aware AI deployment, and inclusive organizational design.
Recommendations synthesized from the reviewed literature and policy analyses; the abstract does not indicate rigorous causal evaluations or quantification of the effectiveness of these interventions within the paper.
There exist successful initiatives, organizational strategies, and policy interventions that have enhanced women’s inclusion, career progression, and representation in emerging tech roles.
Paper reports examples from the reviewed literature and policy analyses that are characterized as 'successful initiatives'; the abstract does not list specific programs, evaluation designs, or sample sizes.
Traditional software engineering artifacts can serve as effective control mechanisms in AI-assisted development.
Concluding claim in the abstract synthesizing the preliminary evaluation results; presented as the paper's implication/recommendation (based on the exploratory study noted).
Embedding machine-readable requirements and architectural artifacts reduces implementation drift.
Reported as a preliminary finding from the exploratory evaluation; the abstract claims a reduction in implementation drift when using Shift-Up artifacts versus unstructured approaches (no quantification provided).
This paper proposes Shift-Up, a framework that reinterprets established software engineering practices (executable requirements / BDD, C4 architectural modeling, and architecture decision records / ADRs) as structural guardrails for GenAI-native development.
Design-science research (DSR) artifact: the Shift-Up framework is presented as the paper's primary design contribution (description/proposal in the paper; no broad empirical validation in the abstract).
Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation.
Stated as a high-level premise in the paper's introduction/abstract; presented as an observed trend motivating the research (no empirical sample or quantified measurement reported in the abstract).
The classical First Fundamental Theorem of Welfare Economics is recovered as the low-autonomy limit of the autonomy-qualified model.
Analytical result in the paper showing limiting case of the model yields the classical theorem (theoretical/mathematical derivation).