AI transparency does not end with a single disclosure
A role-and-output reading of EU AI Act Article 50, translating human-facing disclosure, machine-readable marking, exceptions, and version evidence into one placement record.
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Generative AI
Generative-model use and its cross-cutting effects.
GROUNDING MAP
Questions and evidence in this topic
Explore 22 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
Related research notes
22 ARTICLES
Interviews Suggested About 90% Less Time. More Capacity?
After AI-enabled RPA was introduced, interviews suggested monthly research time fell about 90%. Later, only 10–20% of estimated freed time was explained as new work or extra output. This note shows how to measure the time actually freed and where it went.
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Rethinking RPA: Execution Choices and Operational Handoffs in the AI Era
After work has been redesigned, decide how remaining steps combine core systems, APIs, RPA, AI, and people—and what proves the operation can run without its builder.
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How Far Should Workflows Be Redesigned After AI Adoption?
Treat faster local tasks as only one signal; redesign bottlenecks, exceptions, expert handoffs, and the allocation of released capacity across the full workflow.
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AI use-case selection depends on reallocating validation capacity, not a fixed ranking
Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.
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A Pilot Comparison Design for Moving Generative AI into Production
Compare the existing and AI-assisted workflows under aligned conditions, including human review and revision time, confirmed quality, and unfinished cases. Use a record to decide deployment, hold, retest, or stop.
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The average score of multilingual AI conceals inequities across languages.
Do not deploy based solely on the overall score of translation benchmarks; determine the scope of provision by analyzing culture-dependent items, native-speaker evaluations, worst-case performance per language, and relief pathways.
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Do not approve long-context AI by its nominal limit—set the effective range from position, evidence, and cost.
For owners approving long-document AI, this article compares full text, chunking, and retrieval under changing positions, task types, and noise, then sets an automation boundary from worst-case quality and total verification cost.
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AI reasoning traces are investigative hypotheses, not audit trails
This article demonstrates an audit design that distinguishes between the fluency of Chain-of-Thought and internal judgment reproduction, focusing on answer accuracy, causal fidelity, hidden cues, and separated execution logs.
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AI-text detectors cannot stand alone as evidence for adverse decisions
We design an evidentiary procedure that distinguishes AI text detectors from generation-time watermarks and includes paraphrase resistance, unknown models, group-based false positives, the author's version history, and avenues for appeal.
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Recursive training on synthetic data can lose distribution tails before the average collapses
Instead of uniformly prohibiting the reuse of synthetic data, we design learning data operations that separate the replacement, accumulation, and selection of real data to preserve rare cases and diversity.
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How much should conversational AI be allowed to change human choices?
Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.
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More explanation does not automatically calibrate reliance on AI
Whether AI explanations, confidence displays, judgment order, and user control can truly reconcile the adoption of correct advice with the rejection of incorrect advice is examined by organizing conditions from behavioral experiments.
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The power and water footprint of generative AI cannot be reduced to a per-question figure
Define measurement boundaries for power and water that include usage, processing volume, model, region, time, and equipment, rather than relying on the impression value of a single prompt, and reflect these in procurement and operations.
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Copyright control for generative AI starts with the production process, not the final output
We organize methods for separately viewing training, generation, and publication, and for making rights confirmation and provenance recording a single operational audit trail, drawing from primary sources in Japan, the EU, and C2PA.
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Will generative AI change employment, or will it restructure work first?
We analyze where changes emerge by dividing the impact of generative AI into task time, wages and working hours of existing employees, freelance demand, and youth hiring.
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When does AI coding accelerate development, and when does it slow it down?
Average effects of AI coding are not enough for investment decisions. Research from laboratories, enterprises, and mature open-source projects reveals the work, personnel, and measurement conditions that determine whether development speeds up.
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Does generative AI enhance creativity while homogenizing ideas?
Even if AI assistance improves individual proposals, if the organization's overall proposals become similar, the exploration range narrows. Based on creativity research, we consider methods to manage quality and collective diversity separately.
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Does generative AI deepen learning, or does it merely raise performance during support?
Solving a problem with generative AI is not the same as solving it independently. Drawing on meta-analyses and educational experiments, we examine learning designs that separate supported performance, independent reproduction, and long-term retention.
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Human–AI collaboration can beat humans alone but does not necessarily beat the best solo performer
A meta-analysis of 106 experiments and management studies helps distinguish extension—outperforming humans alone—from true synergy, where collaboration exceeds the best human or AI performance, and identifies when collaboration is viable.
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Does Generative AI Advice Improve Expert Judgment?
We synthesize results from randomized trials and clinical studies involving physicians to clarify the conditions under which generative AI advice improves judgment, and why mere access does not yield effects.
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The ROI of Generative AI Is Determined by Pre-Implementation Design
Do not equate productivity gains demonstrated by research directly with business value. We propose designing ROI by linking time, quality, accuracy, and usage costs at the business process level.
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