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.
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.
Based on evidence that button emphasis, obstruction, defaults, and repeated requests alter choices, audit the procedures and visibility of acceptance, refusal, and withdrawal symmetrically.
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.
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.
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.
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.
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.
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.