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개요
The initial posting deadline was January 1, 2026; developers must also post before each later public release or substantial modification in scope. The law creates transparency duties but does not decide whether particular training uses are lawful.
심층 분석
AB 2013, the Generative Artificial Intelligence Training Data Transparency Act, was enacted in 2024. It requires a developer of a covered generative AI system or service made publicly available to Californians for use to post training-data documentation on its website. The initial posting deadline was January 1, 2026. For a covered system released on or after January 1, 2022, documentation must also be posted before each later public release or substantial modification. The documentation includes a high-level summary of training datasets and information about their sources or owners, purpose, approximate number and types of data points, and collection period. It must address whether datasets include copyrighted, trademarked, patented, personal, or aggregate consumer information; whether they were purchased or licensed; how the developer cleaned or processed them; when they were first used; and whether synthetic data generation was used. The definition of developer includes parties that substantially modify a system for public use. AB 2013 requires dataset-level information, not publication of the raw training corpus or source code. A disclosure does not grant copyright permission, decide fair use, or replace privacy obligations. The act includes exceptions, including systems used solely for security and integrity, systems whose sole purpose is aircraft operation in the national airspace, and systems developed for national-security, military, or defense purposes that are made available only to a federal entity. Read each exception against its exact statutory conditions. Maintain a training-data inventory linked to model versions, fine-tuning runs, synthetic-data generation, and release dates. Assign an owner to prepare and update the public documentation, review sensitive claims, and preserve evidence of what was posted for each covered release. Describe uncertainty rather than inventing dataset provenance. Check the enacted text and current California code for definitions, exceptions, and amendments.
전략적 영향
위험과 안전
치명적인 AI 피해와 일상적인 AI 피해는 누가 위험을 이해하고 누가 조치를 취할 수 있는지에 따라 달라집니다.
더 명확한 결정들
공공 및 전문 지식은 강력한 안전 정책이 정치적으로 가능한지 여부를 결정합니다.
과장된 과장을 뚫고 나가기
명확한 설명은 과대광고, 연구실 홍보, 모호한 윤리 연극에 의한 포착을 줄입니다.
The Future of California AB 2013 Training Data Transparency Law
AB 2013 is operative on a fixed timeline, and implementation choices may be clarified through amendments, enforcement, or litigation. Data pipelines and model versions continue to change, making stale disclosures a practical risk. Developers should monitor official California sources, preserve the dated disclosure in release records, and reassess coverage after substantial model changes. Training-data transparency should be coordinated with privacy, copyright, and contractual review. Retain dated copies of posted documentation and link them to model release records. Recheck the official code after amendments.
실제 구현
A developer lists the sources or owners of training datasets and explains how the data were collected, processed, and used.
A model maker describes whether training included copyrighted, trademarked, or patented material, without claiming that disclosure settles permission questions.
A developer identifies whether personal information was included and whether synthetic data generation was used.
A system developed for national-security, military, or defense purposes is checked against the statutory condition that it be made available only to a federal entity.
위험 및 가드레일
실존적 위험을 공상과학처럼 다루면서 능력을 합성합니다.
높은 자율성 하에서 정렬과 표면 제품 안전성을 혼동합니다.
영어가 아니거나 전문가가 아닌 청중에게는 품질이 낮은 소스만 남겨 둡니다.
구현 로드맵
제품 손상, 오용, 통제력 상실/잘못 정렬 위험을 분리합니다.
일정과 심각도에 대한 귀하의 견해를 바꿀 수 있는 증거가 무엇인지 물어보십시오.
마케팅 주장보다 기본 소스와 구체적인 평가를 선호하세요.
인식뿐만 아니라 경력, 정책, 자금 조달 또는 기술 등 하나의 행동 경로를 식별하십시오.
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자주 묻는 질문
What is California AB 2013 Training Data Transparency Law?
California AB 2013 requires developers of covered generative AI systems and services to publish training-data documentation. The initial posting deadline was January 1, 2026; developers must also post before each later public release or substantial modification in scope. The law creates transparency duties but does not decide whether particular training uses are lawful.
Which developer activity can bring a system within AB 2013?
The statute defines developer broadly to include designing, coding, producing, or substantially modifying a system or service for public use.
Which public documentation deadline does AB 2013 set?
The statute sets January 1, 2026 as the deadline for the required posting.
Which information belongs in the required documentation?
AB 2013 calls for documentation about datasets used in development, including source, collection, processing, and use information.
Does AB 2013 disclosure itself authorize use of copyrighted training material?
The transparency requirement does not resolve copyright permission or fair-use questions.
Which system date can be relevant to coverage?
The statute’s scope includes systems released or substantially modified on or after January 1, 2022.
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