{ "schemaVersion": "1.0", "language": "en", "id": "generative-ai", "name": "Generative AI", "description": "Generative-model use and its cross-cutting effects.", "alternateNames": [ "生成AI" ], "canonicalUrl": "https://catalysphere.co.jp/en/topics/generative-ai/", "machineReadableUrl": "https://catalysphere.co.jp/en/topics/generative-ai/index.json", "updatedAt": "2026-10-01", "articles": [ { "slug": "ai-transparency-disclosure-design", "title": "AI transparency does not end with a single disclosure", "summary": "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.", "quickAnswer": { "what": "Article 50 creates different transparency duties for different roles and system or output categories; one generic badge cannot substitute for them.[1]", "why": "Clarity, timing and accessibility for human-facing disclosure serve a different purpose and implementation surface from machine-readable marking and detectability.[1] [3]", "action": "Catalysphere recommends using a placement record covering the provision, surface, exception basis, test result, version, and owner as a release gate." }, "updatedAt": "2026-09-26", "url": "https://catalysphere.co.jp/en/articles/ai-transparency-disclosure-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-transparency-disclosure-design/index.json" }, { "slug": "ai-automation-capacity-conversion", "title": "Interviews Suggested About 90% Less Time. More Capacity?", "summary": "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.", "quickAnswer": { "what": "In one anonymized workflow, interviews suggested about a 90% time reduction; these figures alone do not justify scaling or recognizing a financial benefit.", "why": "The percentages have different denominators; logs, case volume, quality, and review, correction, and maintenance time were not confirmed, so they do not establish profit or causality.", "action": "Approve only a bounded measurement phase: first check workflow time, quality, and hidden work, then trace where the net freed time went." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-automation-capacity-conversion/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-automation-capacity-conversion/index.json" }, { "slug": "rpa-redesign-operational-handoff", "title": "Rethinking RPA: Execution Choices and Operational Handoffs in the AI Era", "summary": "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.", "quickAnswer": { "what": "RPA is not an obsolete substitute for AI; it is one option for interface work with explicit rules and verifiable completion.", "why": "Core systems, APIs, RPA, AI, and people are not five exclusive choices; they combine different roles in record, connection, action, judgment, and approval.", "action": "Create an execution and handoff acceptance record for each step; test continuity without the builder, then return unmet work to joint operation, narrowed scope, or delayed handoff." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/rpa-redesign-operational-handoff/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/rpa-redesign-operational-handoff/index.json" }, { "slug": "ai-workflow-redesign", "title": "How Far Should Workflows Be Redesigned After AI Adoption?", "summary": "Treat faster local tasks as only one signal; redesign bottlenecks, exceptions, expert handoffs, and the allocation of released capacity across the full workflow.", "quickAnswer": { "what": "After AI adoption, redesign roles by generation, evaluation, exception judgment, and approval rather than replacing an entire job.", "why": "A faster individual step does not increase system outcomes when downstream capacity, review load, demand, and exception handling remain unchanged.", "action": "Measure end-to-end lead time, confirmed quality, rework, exception age, and expert load, then repair the step where congestion moved." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-workflow-redesign/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-workflow-redesign/index.json" }, { "slug": "ai-use-case-portfolio-prioritization", "title": "AI use-case selection depends on reallocating validation capacity, not a fixed ranking", "summary": "Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.", "quickAnswer": { "what": "Select AI use cases as a validation portfolio that improves the next decision, not as a ranking of expected value.", "why": "Benefits, harms, testability, reversibility, and shared infrastructure differ, and a single weighted score hides consequential uncertainty.", "action": "Give each candidate a minimum evidence stage and withdrawal condition, then reallocate budget to scale, hold, redesign, or stop after each test." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/index.json" }, { "slug": "ai-pilot-experiment-design", "title": "A Pilot Comparison Design for Moving Generative AI into Production", "summary": "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.", "quickAnswer": { "what": "Limit production deployment to the scope where a comparison with the existing process supports workflow outcomes and quality.", "why": "Separate access, actual use, drafting speed, review load, and confirmed outcomes; retain unfinished work and unobserved outcomes in the decision.", "action": "Fix assignment, intake, total person-time, quality, follow-up, missingness, and stop authority in a comparison record, and align the measured inputs handed to financial evaluation." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/index.json" }, { "slug": "multilingual-llm-equity", "title": "The average score of multilingual AI conceals inequities across languages.", "summary": "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.", "quickAnswer": { "what": "Evaluation based solely on translation from English may overlook culture-dependent questions and failures in low-resource languages.", "why": "Improving the overall performance of a model does not necessarily automatically narrow the worst-case gap between languages.", "action": "Measure per-language accuracy, abstentions, critical failures, and agreement with human evaluators, and restrict provision for languages that fail to meet the lower bound." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/multilingual-llm-equity/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/multilingual-llm-equity/index.json" }, { "slug": "long-context-reliability", "title": "Do not approve long-context AI by its nominal limit—set the effective range from position, evidence, and cost.", "summary": "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.", "quickAnswer": { "what": "The input limit does not guarantee that necessary evidence can be stably accessed from any position within a long document.", "why": "We must separately evaluate simple string search and business reasoning that connects multiple distant pieces of evidence.", "action": "We compare full-text input, chunking, and retrieval on the same problem set and approve only the range that meets the quality floor and total verification-cost limit." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/long-context-reliability/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/long-context-reliability/index.json" }, { "slug": "chain-of-thought-auditability", "title": "AI reasoning traces are investigative hypotheses, not audit trails", "summary": "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.", "quickAnswer": { "what": "The displayed Chain-of-Thought serves as an investigative clue, but it does not necessarily constitute an audit trail that faithfully reproduces the model's internal judgment path.", "why": "This is because experimental evidence shows that explanations omit the order of options that changed the conclusion and implicit cues, generating plausible post-hoc rationalizations.", "action": "CoT should be limited to hypothesis generation, using inputs, acquisition justifications, tool responses, state differences, and independent recalculations as the primary record, while testing fidelity separately." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/chain-of-thought-auditability/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/chain-of-thought-auditability/index.json" }, { "slug": "ai-generated-text-detection-evidence", "title": "AI-text detectors cannot stand alone as evidence for adverse decisions", "summary": "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.", "quickAnswer": { "what": "AI text detection scores do not prove generation origin and should be limited to one piece of material for initiating investigations in high-stakes disciplinary actions.", "why": "This is because post-hoc detection fails under paraphrasing and unknown distributions, while generation-time watermarks depend on provider implementation, keys, text length, and modifications.", "action": "We establish upper limits for false positives by use case, merge version history, citations, work logs, and author explanations, and implement independent re-review and correctable appeal pathways." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-generated-text-detection-evidence/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-generated-text-detection-evidence/index.json" }, { "slug": "synthetic-training-data-model-collapse", "title": "Recursive training on synthetic data can lose distribution tails before the average collapses", "summary": "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.", "quickAnswer": { "what": "Synthetic data does not automatically cause model collapse, but recursive learning that replaces real data can lose the tails of the distribution and diversity.", "why": "This is because degradation depends not on the material name, but on how the retention of real data, reuse across generations, sample selection, and errors from finite data overlap.", "action": "Preserve every sample’s provenance and generation, test rare groups, tails and diversity separately from average performance, and quarantine deteriorating sources or categories before restoring the verified baseline and retesting." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/synthetic-training-data-model-collapse/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/synthetic-training-data-model-collapse/index.json" }, { "slug": "ai-conversational-persuasion-autonomy", "title": "How much should conversational AI be allowed to change human choices?", "summary": "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.", "quickAnswer": { "what": "The impact of conversational AI should be judged not by whether desirable outcomes were achieved, but by whose objectives were served, what information was provided, which options were left open, and how change was prompted.", "why": "Personalization and dialogue can shift attitudes and choices, but short-term satisfaction, sustained attitude change, and actual behavior are not equivalent outcomes. [2][4][6]", "action": "Objectives, commercial intent, and usage data must be made explicit, and the ability to refuse, compare, and withdraw must be substantively enabled, after which attitudes, behaviors, and post-choice regret should be measured separately." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/index.json" }, { "slug": "ai-reliance-calibration-behavioral-science", "title": "More explanation does not automatically calibrate reliance on AI", "summary": "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.", "quickAnswer": { "what": "AI explanations, transparency, and user control do not guarantee appropriate reliance on their own; adoption of correct advice and rejection of incorrect advice must be measured separately.", "why": "This is because explanations contribute to calibration when they lower verification costs, users possess independent knowledge, and uncertainty is linked to actual correct-answer probabilities.", "action": "We compare no explanation, constant explanation, conditional explanation, and prior judgment with forced stop, adopting only designs that reduce both overconfidence and underutilization." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/index.json" }, { "slug": "generative-ai-energy-water", "title": "The power and water footprint of generative AI cannot be reduced to a per-question figure", "summary": "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.", "quickAnswer": { "what": "The IEA's 2026 update presents a central forecast that global data center power consumption will approximately double from 485 TWh in 2025 to 950 TWh by 2030.[1]", "why": "The U.S. 2030 estimate is 649 TWh for the base case, accounting for 11.8% of total power, and water cannot be managed with a single conversion value because it varies by model, equipment, region, and time.[2] [3]", "action": "Compare processing volumes and alternatives on a use-case basis, obtain the measurement scope, region, period, and estimation method for power and water from the provider, and evaluate them in the same meeting as quality and cost." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-energy-water/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-energy-water/index.json" }, { "slug": "generative-ai-copyright-provenance", "title": "Copyright control for generative AI starts with the production process, not the final output", "summary": "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.", "quickAnswer": { "what": "We manage the copyright risks of generative AI as a series of processes spanning not only the output but also the training data, prompts and editing, and methods of publication. [1] [2]", "why": "Legal judgment and technical provenance serve different roles; while C2PA supports the detection of alterations to provenance information, it does not guarantee the legality of rights processing itself. [3]", "action": "For each case, we record the basis of rights, terms of service, human creative contribution, generation history, and disclosures at publication in the same ledger, and if doubts arise, we halt publication and consult experts." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-copyright-provenance/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-copyright-provenance/index.json" }, { "slug": "generative-ai-labor-market-effects", "title": "Will generative AI change employment, or will it restructure work first?", "summary": "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.", "quickAnswer": { "what": "The labor impact of generative AI may not manifest as a simultaneous reduction in employment, but rather first in the allocation of work, outsourcing demand, and youth hiring.", "why": "This is because task time reduction and corporate employment, wages, and hiring follow different adjustment pathways and timelines.", "action": "We continuously monitor not only headcount but also hiring, mobility, time allocation by job, and new supervisory tasks." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-labor-market-effects/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-labor-market-effects/index.json" }, { "slug": "ai-coding-productivity-context", "title": "When does AI coding accelerate development, and when does it slow it down?", "summary": "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.", "quickAnswer": { "what": "While AI coding may accelerate tasks with clear specifications that are easy to verify, it can slow down maintenance work requiring deep contextual understanding.", "why": "This is because the burden of explanation, waiting, verification, review, and correction varies by task, not just the generation time of the output.", "action": "We categorize work by verifiability and contextual depth, comparing the period from initiation to post-merge rework with and without AI assistance." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/ai-coding-productivity-context/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-coding-productivity-context/index.json" }, { "slug": "generative-ai-creativity-diversity", "title": "Does generative AI enhance creativity while homogenizing ideas?", "summary": "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.", "quickAnswer": { "what": "Generative AI may enhance the evaluation of individual proposals while making the group's proposals similar, thereby narrowing the exploration range.", "why": "This is because when many people are guided toward known patterns of the same model, average quality and collective diversity move in opposite directions.", "action": "Preserve independent proposals before AI use, assign roles with different premises, and evaluate quality and proposal overlap rates separately." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-creativity-diversity/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-creativity-diversity/index.json" }, { "slug": "generative-ai-learning-design", "title": "Does generative AI deepen learning, or does it merely raise performance during support?", "summary": "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.", "quickAnswer": { "what": "Even if generative AI improves performance during practice, it does not necessarily improve understanding, retention, or transfer to other tasks.", "why": "This is because support that provides answers quickly reduces cognitive load but may also reduce opportunities for necessary thinking and learning from errors.", "action": "Stage hints, administer immediate and delayed tests without AI, and measure performance during support separately from learning." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-learning-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-learning-design/index.json" }, { "slug": "human-ai-synergy-meta-analysis", "title": "Human–AI collaboration can beat humans alone but does not necessarily beat the best solo performer", "summary": "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.", "quickAnswer": { "what": "Collaboration between humans and AI does not necessarily exceed the performance of the best individual human or AI, even if it performs better than humans alone.", "why": "This is because the added value of the combination disappears when strengths overlap or when humans adopt incorrect advice.", "action": "We divide generation, evaluation, selection, and final responsibility, and verify synergy through comparative tests that include the benchmark for the best individual performance." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/human-ai-synergy-meta-analysis/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/human-ai-synergy-meta-analysis/index.json" }, { "slug": "llm-professional-decision-quality", "title": "Does Generative AI Advice Improve Expert Judgment?", "summary": "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.", "quickAnswer": { "what": "Generative AI advice does not automatically improve expert judgment; it can yield benefits or harms depending on the task and how it is used.", "why": "Even if AI performance alone is high, collaborative outcomes depend on whether humans can detect incorrect advice and whether the interface prompts verification.", "action": "Compare AI-assisted versus unassisted performance on the same cases, separating accuracy, time, misses, and correction rates to determine whether to adopt it." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/llm-professional-decision-quality/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/llm-professional-decision-quality/index.json" }, { "slug": "generative-ai-roi-design", "title": "The ROI of Generative AI Is Determined by Pre-Implementation Design", "summary": "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.", "quickAnswer": { "what": "The ROI of generative AI should be judged by business outcomes, including quality, rework, and total cost, not just time savings.", "why": "Because the effectiveness of the same AI can reverse depending on task difficulty, user proficiency, and verification processes.", "action": "Fix baseline metrics and criteria for continuation or termination at the business process level before implementation, and begin with small comparative validations." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/generative-ai-roi-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-roi-design/index.json" } ] }