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Countries speak the same AI ethics language but govern very differently: a comparative analysis of 24 policy texts finds five distinct models of AI governance—from Europe’s rights-first approach to China’s state-centric model—driven by underlying political-economic institutions rather than convergent ethical principles.
low descriptive semantic_scholar
Artificial intelligence governance and social policy divergence: a comparative political economy perspective on global AI regulation · Deepak Kumar, Chinmaya Kumar Sahu · Fetched Mar 30, 2026
Agentic AI could halve investment-management costs and threaten large-scale displacement among financial planners; the authors propose mandatory AI literacy, specialized degree tracks, federal retraining and income support, and regulatory incentives to steer a 5–15 year workforce transition.
low descriptive semantic_scholar
STRENGTHENING FINANCIAL WORKFORCE COMPETITIVENESS: A CURRICULAR FRAMEWORK FOR INTEGRATING AGENTIC AI INTO FINANCIAL PLANNING EDUCATION · Satyadhar Joshi · Fetched Mar 30, 2026
Agentic AI is only beginning to surface in U.S. finance firms' annual reports—0.4% of firms in 2024 and 1.6% in 2025—but those that mention agentic systems tend to surround them with denser governance and safety language, implying deployments are currently concentrated where governance is more mature.
n/a descriptive semantic_scholar
Measuring agentic AI adoption and control frameworks in finance · Atta ul Mustafa, A. Aysan · Fetched Mar 30, 2026
A model warns that consumer demand switching to AI services can widen wage gaps in non-traded sectors, but Indian transport data show no clear post-2007 spike in inequality — uptake of AI ride services stayed below 0.7%, muting measurable effects.
low theoretical semantic_scholar
Artificial Intelligence, Demand Switching and Sectoral Wage Gap · Shreya Roy, Bibek Ray Chaudhuri · Fetched Mar 30, 2026
Making an AI transparent doesn't automatically win more data: transparency raises stated willingness to share only for users who already trust AI, while immediate sharing decisions remain high and unchanged across human, white-box, and black-box scenarios.
medium rct openalex
Understanding Data-Sharing with AI Systems: The Roles of Transparency, Trust, and the Processing Entity · Julian Rosenberger, Sophie Kuhlemann, Verena Tiefenbeck, Mathias Kraus, Patrick ... · Fetched Mar 30, 2026
A five‑node value chain and 'Innovation Frontier' map how AI is reshaping legal work, arguing that targeted oversight can contain professional and systemic risks while enabling broader access and quality gains in legal services.
n/a commentary openalex
Rewired: Reconceptualizing Legal Services for the AI Age · Lev Breydo · Fetched Mar 30, 2026
Companies that treat AI as a strategic operating layer — deploying agentic workflows and localized models — can accelerate innovation and expand internationally without traditional physical footprints. This advantage, however, only materializes when firms transition from pilot projects to integrated, governed AI‑first business models.
n/a descriptive openalex
The AI Advantage: Strategic Innovation and Global Expansion in the Digital Era · Yashaswini Bergal, Rupali Dilip Taru · Mar 29, 2026
AI delivers bigger growth payoffs where institutions adapt: countries with stronger institutions, higher digital capacity and R&D intensity see superior productivity performance, suggesting AI's benefits depend on institutional and organizational innovation.
low theoretical semantic_scholar
Artificial intelligence, institutional innovation and economic growth: A conceptual framework · Zehra Doğan Çalışkan · Fetched Mar 28, 2026
AI-powered, multi-source horizon scanning classifies technologies into emerging, developing and mature stages and matches each stage to tailored policy tools—R&D funding, regulatory sandboxes, procurement incentives and tax relief. Expert Delphi validation and AHP weighting increase prioritization confidence, though outcomes depend on data coverage and expert selection.
low descriptive semantic_scholar
Emerging Technologies Based on Large AI Models and the Design of Support Policies: Patent Mining and Industry Trends · Yiwei Zhang, Shanghai Mu, Baitong Liu · Fetched Mar 28, 2026
Closed AI models often undermine scientific inference by hiding construction and deployment details, making results hard to interpret, replicate, or generalize; researchers should explicitly document threats to inference, the mitigation steps taken, and why a given (closed or open) model was chosen.
n/a theoretical arxiv
How Open Must Language Models be to Enable Reliable Scientific Inference? · James A. Michaelov, Catherine Arnett, Tyler A. Chang, Pamela D. Rivière, Samuel ... · Mar 27, 2026
An ASR system trained on field recordings of Ikema achieves around 15% character error rate and materially speeds up human transcription, cutting labor time and reducing cognitive load; this suggests practical AI-assisted pathways for scaling documentation of severely endangered languages.
medium quasi_experimental arxiv
Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan · Chihiro Taguchi, Yukinori Takubo, David Chiang · Mar 27, 2026
AI is reshaping Cambodia's labour market: low-skilled workers face disproportionate displacement while new industries create employment and spike demand for reskilling; policymakers must invest in targeted training and education.
low correlational semantic_scholar
Navigating AI‐Induced Job Displacement and Skill Demands: Insights From an Emerging Market Perspective · Bora Ly, Romny Ly, Sokhom Ma · Fetched Mar 27, 2026
AI code reviewers currently catch only a fraction of issues human experts find — top models detect 15–31% of flagged problems — and giving them more file-level context often makes performance worse, suggesting attention dilution in long prompts.
high descriptive arxiv
SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback · Deepak Kumar · Mar 27, 2026
Generative AI boosts customer-service throughput and agent productivity, especially by automating routine queries and drafting replies, but quality failures and trust risks mean it complements rather than replaces humans; hybrid human-AI systems with strong governance produce the best results.
medium review_meta openalex
The Effectiveness of ChatGPT in Customer Service and Communication Management (Nano Review) · Horn Sarun · Mar 27, 2026
Generative AI widens the space of feasible ideas and speeds early-stage R&D, but gains crystallize only when humans vet, curate and implement outputs; unchecked use risks low-quality, biased, or risky innovations.
medium review_meta openalex
ChatGPT as an Innovative Tool for Idea Generation and Problem-Solving: Nano Review · Horn Sarun · Mar 27, 2026
Generative AI floods teams with ideas and structures early-stage problem solving but seldom substitutes for human judgment; firms that combine high-volume generation with expert curation will capture most of the economic upside, while policy should prioritize training and accountability.
medium review_meta openalex
ChatGPT as an Innovative Tool for Idea Generation and Problem-Solving: Nano Review · Horn Sarun · Mar 27, 2026
Generative AI can cut costs and scale personalized customer service, but hallucinations, empathy shortfalls and integration hurdles make full automation unwise; firms capture the most value through hybrid human–AI systems with transparent AI use, clear escalation paths and continuous oversight.
low review_meta openalex
The Effectiveness of ChatGPT in Customer Service and Communication Management (Nano Review) · Horn Sarun · Mar 27, 2026
Behavioral frictions—miscalibrated trust, cognitive overload, and weak governance—help explain why many corporate AI pilots never reach production, with coding assistants a high‑visibility example. The paper offers a practical diagnostic framework for managers to detect disengagement and guide institutionalization, alongside a research agenda for mixed‑methods validation.
n/a review_meta openalex
Behavioral Factors as Determinants of Successful Scaling of Artificial Intelligence Pilot Projects in a Corporate Environment · Aneesha Sharma · Mar 27, 2026
China's AI collaboration network grows through triangle-closing and 'rich-get-richer' dynamics and is driven more by universities and research institutes than by firms; notably, organizations seek complementary technological expertise rather than similar tech, while geographic, cultural and institutional closeness still helps form ties.
medium correlational openalex
The evolutionary mechanism of artificial intelligence industry collaboration networks: evidence from China · Jianlin Lyu, Bin Hu, Wenrong Lyu · Mar 27, 2026
Smarter search can hurt consumers: more informative AI-assisted search may reduce learning and consumer surplus and soften price competition unless markets themselves learn from agent-consumer interactions; by contrast, cheaper search unambiguously benefits consumers.
n/a theoretical arxiv
Agentic Markets: Equilibrium Effects of Improving Consumer Search · Brendan Lucier, Nicole Immorlica, Markus Mobius, Aleksandrs Slivkins, Daniel G. ... · Mar 26, 2026
A new 67.5-hour benchmark finds current multimodal models struggle to infer users' GUI behavior and intent — scoring about 44.6% on behavior-state detection and 55.0% on help prediction — yet supplying structured user context can raise help-prediction accuracy by up to 50.2 percentage points.
n/a descriptive arxiv
GUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI Tasks · Saelyne Yang, Jaesang Yu, Yi-Hao Peng, Kevin Qinghong Lin, Jae Won Cho, Yale Son... · Mar 26, 2026
Multiple general-purpose coding agents can dramatically accelerate hardware designs: a two-stage agent factory using up to 10 Claude Code agents achieves a mean 8.27× HLS speedup across 12 benchmarks, with peaks above 20×, and finds cross-kernel optimizations missed by per-subkernel search.
medium other arxiv
Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization? · Abhishek Bhandwaldar, Mihir Choudhury, Ruchir Puri, Akash Srivastava · Mar 26, 2026
EcoThink slashes LLM inference energy by about 40% on average by routing simple queries around costly reasoning routines, preserving performance while lowering operational carbon and promising broader access for resource-constrained users.
medium descriptive arxiv
EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents · Linxiao Li, Zhixiang Lu · Mar 26, 2026
Model retraining should be driven by a loss‑minimizing 'learning debt' threshold, not a calendar; a Bayesian decision‑theoretic framework yields auditable, evidence‑based retraining triggers that trade off performance drift against computational and operational cost.
n/a theoretical arxiv
Retraining as Approximate Bayesian Inference · Harrison Katz · Mar 26, 2026
DRL-driven autonomous vehicles raise highway capacity by roughly 7.5% and slash fuel use at high speeds by about 29% compared with a standard car-following model in NGSIM-based simulations. The benefits are strongly dependent on drivers' time-gap distributions and the share of RL-controlled vehicles, and derive from simulated mixed-traffic experiments rather than real-world trials.
medium descriptive arxiv
Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles · Pankaj Kumar, Pranamesh Chakraborty, Subrahmanya Swamy Peruru · Mar 26, 2026