Papers (1747)

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AI is reshaping tech workplaces but will widen or narrow gender gaps depending on how it is deployed; bias-aware algorithms, targeted reskilling and inclusive organisational design consistently improve women's participation and career progression.

medium review_meta Summary semantic_scholar

Artificial Intelligence and GenderedEmployment: Reviewing Opportunities andChallenges for Women in Emerging TechnologySectors · Jay Patel · Fetched Apr 26, 2026

AI and robots are hollowing out routine low‑skilled roles and exerting downward wage pressure, even as augmentative AI boosts productivity for some — a dynamic that is widening inequality and spurring unions and governments to press for stronger workplace technology governance.

n/a review_meta Full text semantic_scholar

From Technological Substitution to Institutional Response: A Systematic Review of Anxiety, Resistance, and Governance Transformation among Low Skilled Workers in the Age of Artificial Intelligence · Weixiang Gan, Mengfei Xiao, Tara Ahmed Mohammed, Qiuying Yue, Yongli Zhang · Fetched Apr 26, 2026

Existing legal and technical frameworks cannot reliably identify or hold autonomous AI agents accountable; five fundamental gaps — from intent verification to recursive delegation accountability — mean engineering work alone will not solve the problem. Foundational research on agent identity is urgently needed to enable safe cross‑organizational AI transactions.

n/a review_meta Full text arxiv

AI Identity: Standards, Gaps, and Research Directions for AI Agents · Takumi Otsuka, Kentaroh Toyoda, Alex Leung · Apr 25, 2026

Racing to build domestic AI industries without embedding democratic economic governance risks consolidating corporate control of critical technologies and weakening democratic objectives. Policymakers should reorient AI industrial strategies toward competition, public purpose, and worker and community power to ensure AI serves broad social goals rather than narrow corporate interests.

n/a commentary Summary semantic_scholar

Fighting for Democracy Amid the AI Race: Designing Tech Industrial Policy to Promote Democratic Economic Governance · Max von Thun, Audrey Stienon · Fetched Apr 25, 2026

Automated state systems boost administrative efficiency but erode democratic legitimacy: AI improves routine tasks and fiscal forecasting yet opaque algorithms (notably Australia’s Robo‑debt) produce procedural injustice. Without value-based transparency and role-sensitive explainability, the rise of algorithmic governance deepens an AI Core–Global South divide and risks long-term digital dependency.

medium review_meta Summary semantic_scholar

Artificial Intelligence, Public Policy and Governance - implications for Economic Management and Political Systems · Glory Mmerechi Triumph Okereke, Philip Williams Appiah-Agyei · Fetched Apr 25, 2026

Pipeline-driven broadband expansion unlocked AI adoption in Turkey, with the biggest gains among small and software-heavy firms; increased connectivity not only raised AI use but also boosted productivity and exports while shifting employment toward ICT roles.

high quasi_experimental Summary semantic_scholar

Digital Infrastructure, AI Adoption, and Firm Performance * · Nuriye Melisa Bilgin, G. Ottaviano · Fetched Apr 25, 2026

AI is already helpful for routine, data-heavy engineering tasks, but wider gains depend less on model intelligence than on plumbing: fragmented data, closed legacy toolchains, and verification and governance gaps block higher-order automation. Firms expect a staged progression from assistive tools to trustworthy agentic workflows as infrastructure, auditability, and organizational capabilities mature.

low descriptive Full text semantic_scholar

Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities · Kristen M. Edwards, M. Bauer, Claire Jacquillat, A. J. Hart, Faez Ahmed · Fetched Apr 25, 2026

ByteDance used AI that learns across products and an adaptive organizational structure to scale and diversify at once, reversing the usual trade-off between specialization and expansion; diversification amplified the value of its AI assets rather than diluting them.

low descriptive Summary semantic_scholar

Scaling high and wide: How firms leverage AI and organizational design to overcome the scale‐scope trade‐off · Feng Wan, Tianxi Yang, Xianwei Shi, Ke Rong, S. Ansari · Fetched Apr 25, 2026

Generative AI boosts management accounting by enriching data and sharpening analysis rather than substituting judgment; its benefits appear only when tied into standardized routines, integrated data infrastructures and strong cross‑functional governance, though evidence comes from public disclosures rather than internal causal proof.

low descriptive Full text semantic_scholar

Research on the Impact of Generative AI on the Quality of Management Accounting Decisions: Evidence from Manufacturing Firms · Xintian Shen · Fetched Apr 25, 2026

Complex human-AI tasks can lower employee engagement by raising tech-learning anxiety, but confidence with AI and humble leaders blunt the harm.

low correlational Full text semantic_scholar

How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy. · Boli Wang, Simeng Liu, Chenhao Luo · Fetched Apr 25, 2026

An AI-augmented visual knowledge-graph interface helps users build clearer conceptual structure: in a 12-person lab study, MindTrellis yielded higher expert ratings for coverage and structure and reduced cognitive load compared with retrieval-only tools. The result suggests that jointly constructed, editable knowledge representations can improve individual information synthesis in short-term tasks.

low quasi_experimental Full text arxiv

MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration · Xiang Li, Cara Li, Emily Kuang, Can Liu, Jian Zhao · Apr 25, 2026

Generative AI can act as a cognitive collaborator—boosting idea generation, synthesis and strategic framing—but may also blunt intrinsic motivation and evaluative rigor, creating a 'Creativity Paradox' that firms must design around.

n/a theoretical Full text openalex

Beyond the Creativity Paradox: A Theory-informed Framework for Role-based Integration of Generative AI in Organisational Creativity · Youngseok Choi, Chang Won Park, Ceyda Paydas Turan, Habin Lee · Apr 25, 2026

Industrial robots have only modest links to job quality across Europe once controls are included, but they reduce physical risks and appear to narrow the gender gap in autonomy while women still face higher work intensity.

medium correlational Full text openalex

Gendered Effects of Robotisation on Job Quality · Dagmara Nikulin · Apr 25, 2026

Improved AI can reduce firm deployment when weak governance forces broader authority exposure, creating a 'deployment paradox' where capability gains fail to translate into productive use; stronger governance that cuts breach losses restores deployment, while externalities make social under-use more pervasive.

n/a theoretical Full text arxiv

The Security Cost of Intelligence: AI Capability, Cyber Risk, and Deployment Paradox · Sukwoong Choi · Apr 24, 2026

AI coding agents often burn far more tokens than expected — input tokens drive costs and usage can vary 30x between runs; some models consume millions more tokens than others and none reliably predict their own token bill.

medium descriptive Full text arxiv

How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks · Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfss... · Apr 24, 2026

Shapley explanation variants raise analyst confidence without improving accuracy: in 3,735 fraud-case reviews, popular quantitative explanation proxies (sparsity, faithfulness) did not predict human-perceived clarity or decision utility, creating a risk of automation bias in operational decision systems.

medium quasi_experimental Full text arxiv

Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings · Inês Oliveira e Silva, Sérgio Jesus, Iker Perez, Rita P. Ribeiro, Carlos Soares,... · Apr 24, 2026

Pretrained ‘foundation’ time‑series models beat bespoke forecasts across 54 energy datasets, with largest gains in covariate-rich and highly aggregated series. Accuracy improvements are robust across categories, correlate with spectral entropy, and show limited returns beyond a moderate context length.

medium descriptive Full text arxiv

FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting · Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair · Apr 24, 2026

Refinement loops often hurt unless the model almost never introduces new errors: iterative self-correction degrades accuracy for many LLMs unless EIR is near zero, but a lightweight 'verify-first' prompt can reduce EIR and restore gains on affected models.

medium quasi_experimental Full text arxiv

When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention · Aofan Liu, Jingxiang Meng · Apr 24, 2026

Revealing a few tailored features to humans — rather than giving full predictions — can boost human–algorithm teamwork, but designing highlights for fully rational users is computationally infeasible; optimizing for more realistic, 'naive' users yields tractable and robust gains in calibrated housing-data simulations.

n/a theoretical Full text arxiv

Algorithmic Feature Highlighting for Human-AI Decision-Making · Yifan Guo, Jann Spiess · Apr 24, 2026

A learning-augmented robot ran a live motor assembly station for over five hours, producing 108 motors with a 99.4% quality pass rate and near-human cycle times while operating without physical fencing.

medium descriptive Full text arxiv

Learning-augmented robotic automation for real-world manufacturing · Yunho Kim, Quan Nguyen, Taewhan Kim, Youngjin Heo, Joonho Lee · Apr 24, 2026

Machine-learning contests invite strategic 'benchmark hacking': lower-skill entrants divert effort into task-specific tuning rather than true capability gains, while higher-skill entrants do not. Concentrating prizes among top finishers reduces such gaming and yields better generalization.

medium theoretical Full text arxiv

On Benchmark Hacking in ML Contests: Modeling, Insights and Design · Xiaoyun Qiu, Yang Yu, Haifeng Xu · Apr 24, 2026

Human–AI relations should be designed as governed co-evolution, not obedience: a formal 'conditional mutualism' model shows reciprocal, regulated coupling produces stable coexistence and fairer outcomes, whereas ungoverned interactions risk lock-in, fragility and domination.

n/a theoretical Full text arxiv

A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies · Somyajit Chakraborty · Apr 24, 2026

Bias in AI hiring often emerges from interactions across fragmented vendor–platform–employer supply chains, not single components; regulators and firms should mandate cross-supplier documentation, system-level audits, and continuous monitoring to restore meaningful accountability.

n/a review_meta Full text openalex

How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications · Gauri Sharma, Maryam Molamohammadi · Apr 24, 2026

Fix a numerical safety target, then test: the paper proposes RoMA/gRoMA black‑box statistical tests that let developers produce auditable upper bounds on AI failure rates without revealing model internals, enabling conformity assessments aligned with laws like the EU AI Act.

n/a theoretical Full text arxiv

Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation · Natan Levy, Gadi Perl · Apr 23, 2026

AEL — a two-timescale approach pairing bandit-guided memory retrieval with LLM-driven reflection — lifts trading-agent Sharpe to 2.13 and reduces variance on a 208-episode portfolio benchmark, beating prior self-improving methods; notably, adding further architectural complexity harms performance, suggesting the bottleneck is diagnosing how to use past experience rather than more components.

medium other Full text arxiv

AEL: Agent Evolving Learning for Open-Ended Environments · Wujiang Xu, Jiaojiao Han, Minghao Guo, Kai Mei, Xi Zhu, Han Zhang, Dimitris N. M... · Apr 23, 2026

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