{ "schemaVersion": "1.0", "language": "en", "id": "privacy-rights", "name": "Privacy & Rights", "description": "Personal data, training data, copyright, and provenance.", "alternateNames": [ "プライバシー・権利", "Data minimization", "Content provenance", "AI Privacy", "Content Provenance", "Learning data", "Data Minimization", "Training Data", "Synthetic Data", "Privacy", "Differential Privacy", "Data Sharing", "Copyright" ], "canonicalUrl": "https://catalysphere.co.jp/en/topics/privacy-rights/", "machineReadableUrl": "https://catalysphere.co.jp/en/topics/privacy-rights/index.json", "updatedAt": "2026-10-01", "articles": [ { "slug": "ai-agent-audit-log-minimization", "title": "More audit logs do not make agent actions reconstructable", "summary": "Design evidence for AI-agent actions around a chain of approval, execution, and state change—not full-content retention. Capture disputed content selectively under protection, then test reconstructability and minimization as separate release gates.", "quickAnswer": { "what": "Decide first whether the target is approval, state change, or disputed transmitted content; each reconstruction question has different evidence and authoritative records.", "why": "Join structured events by default, and capture exact content only for defined disputes under explicit scope, access, protection, and expiry controls.", "action": "Production acceptance requires a separate evidence-sufficiency gate and a minimization gate covering purpose, access, retention, exceptions, and tested disposition." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-agent-audit-log-minimization/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-agent-audit-log-minimization/index.json" }, { "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": "dark-patterns-ai-consent", "title": "Is consent to AI as easy to refuse as it is to accept?", "summary": "Based on evidence that button emphasis, obstruction, defaults, and repeated requests alter choices, audit the procedures and visibility of acceptance, refusal, and withdrawal symmetrically.", "quickAnswer": { "what": "Multiple dark patterns, such as obstruction, scarcity, and social proof, have been observed in online services.", "why": "Asymmetry in choice screens can increase consent or purchases, while strong design may lead to backlash or reduced understanding of choices.", "action": "Symmetrize the number of steps, visibility, understanding, and withdrawal for acceptance and refusal, and conduct comparisons with neutral screens and post-hoc remedies." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/dark-patterns-ai-consent/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/dark-patterns-ai-consent/index.json" }, { "slug": "machine-unlearning-verification", "title": "How can one prove that AI has forgotten?", "summary": "Do not treat the completion of a deletion API as proof of forgetting; instead, comprehensively verify target memory, retention utility, leakage, and continuous deletion by using a retrained model as the baseline.", "quickAnswer": { "what": "Merely ceasing to answer a specific question does not prove that the influence of training data has disappeared or that the possibility of leakage has been eliminated.", "why": "Compare forgetting, retention utility, privacy, and continuous deletion against a model retrained without the target data.", "action": "In cases where guarantees are insufficient, do not describe approximate deletion as complete deletion, and keep retraining or model disposal as options." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/machine-unlearning-verification/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/machine-unlearning-verification/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-privacy-data-minimization", "title": "Data Minimization in the AI Era: Do Not Collect, Do Not Retain, Do Not Overlap", "summary": "We do not leave AI privacy measures solely to post-collection anonymization; we reduce necessity, retention, duplication, learning, logging, and access across the entire lifecycle.", "quickAnswer": { "what": "Data minimization is not about reducing volume, but is the principle of handling only the personal data necessary for the purpose, at the required precision and duration.", "why": "This is because large models can replay training data, and duplication within the same sequence significantly amplifies the risks of memorization and extraction.", "action": "We operate necessity review before collection, deduplication, retention periods, log control, extraction testing, and deletion pathways through a single ledger." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-privacy-data-minimization/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-privacy-data-minimization/index.json" }, { "slug": "synthetic-data-privacy-utility", "title": "Synthetic Data Is Not Anonymized: Designing Privacy and Utility", "summary": "The mere format of artificial generation does not guarantee safety; we address attackers, memorization, differential privacy, and utility by use case in a single evaluation table.", "quickAnswer": { "what": "Synthetic data is a name for a generation method and does not automatically imply that original personal information cannot be inferred or extracted.", "why": "This is because retaining a similar distribution can leave traces of individual records, and safety and utility depend on the use case, attack, and budget.", "action": "First select the threat model and privacy guarantee, then compare attack tests and downstream task evaluations under the same conditions." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/synthetic-data-privacy-utility/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/synthetic-data-privacy-utility/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" } ] }