US · AI law tracker
HR 8094 — US
HR 8094 is an AI governance legislation from US, currently in committee. US House bill H.R. 8094 proposes new FTC requirements for public transparency regarding AI foundation model training data and algorithms [1]. AIGI tracks 3 primary-source updates on this bill; the most recent was published on 2026-04-29.
Status & timeline
- Regulatory stage
- in committee
- Bill status
- in committee
- Effective date
- 2026-04-29
- Authority / governing body
- Council of the Inspectors General on Integrity and Efficiency
- Chamber
- house
- Document type
- legislation
Next deadline: No fixed deadline — referred to committee for consideration.
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Full obligation matrix
| Actor | Obligation | Deadline | Source |
|---|---|---|---|
| provider | Make information available to the public about the training data used in artificial intelligence foundation models. | To be established by FTC rulemaking | — |
| provider | Make information available to the public about the algorithms used in artificial intelligence foundation models. | To be established by FTC rulemaking | — |
| deployer | Make information available to the public about the training data used in artificial intelligence foundation models. | To be established by FTC rulemaking | — |
| deployer | Make information available to the public about the algorithms used in artificial intelligence foundation models. | To be established by FTC rulemaking | — |
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Enforcement risk score
Announced regulation; enforcement footprint still forming.
Subscriber only
Role-based compliance checklist
- compliance_officer Monitor the progress of HR 8094 through Congress and any subsequent FTC rulemaking.
- cto Assess current capabilities for documenting and disclosing information about AI foundation model training data and algorithms.
- legal Evaluate the legal implications of potential FTC requirements for AI foundation model transparency and advise on compliance strategy.
- product_manager Consider how future transparency requirements might impact product design, feature development, and public-facing documentation for AI models.
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Vendor impact assessment
- Vendor risk class
- high
- Procurement categories
- foundation_models, ai_risk_management, productivity_assistants, customer_service_ai
Vendors providing AI foundation models or AI systems built upon them will likely need to develop robust internal processes for documenting and disclosing information about their training data and algorithmic design. This could significantly impact product offerings and require clear communication with clients.
Sample vendor questions
- What information can your organization provide regarding the training data used in your AI foundation models?
- How are the algorithms within your AI foundation models documented, and what details can be shared publicly or with customers?
- What processes are in place to ensure ongoing transparency regarding changes or updates to your AI foundation models' training data or algorithms?
- How do your AI solutions help us comply with potential future transparency requirements for AI foundation models?
Intelligence briefs (3)
US House Bill H.R. 8094 Proposes FTC Rules for AI Foundation Model Transparency
US House bill H.R. 8094 proposes new FTC requirements for public transparency regarding AI foundation model training data and algorithms [1].
This signals a potential federal push for transparency obligations on developers and deployers of foundation models under FTC oversight, influencing AI governance frameworks.
Deadline: No fixed deadline — referred to committee for consideration.
Primary source →US House Bill Directs FTC to Set AI Foundation Model Transparency Rules
A House bill was introduced to mandate FTC rulemaking on AI foundation model transparency [1].
This signals a potential federal shift toward greater transparency obligations for AI foundation models, impacting deployer disclosure standards.
Deadline: No fixed deadline — regulatory process initiated.
Primary source →US House Bill Proposes Transparency for AI Foundation Models
A US House bill proposes new transparency requirements for AI foundation models [1].
This signals a potential future expansion of federal AI governance frameworks concerning model transparency and accountability.
Deadline: No fixed deadline — proposal under consideration.
Primary source →Frequently asked questions
- What is HR 8094?
- H.R. 8094, introduced in the US House of Representatives, proposes to direct the Federal Trade Commission (FTC) to establish requirements for making information available to the public about the training data and algorithms used in artificial intelligence foundation models [1]. The bill has been referred to the Committee on Energy and Commerce, signaling an early stage in the legislative process for potential federal AI transparency mandates. Primary source →
- Why does HR 8094 matter?
- This signals a potential federal push for transparency obligations on developers and deployers of foundation models under FTC oversight, influencing AI governance frameworks. Primary source →
- Who does HR 8094 affect?
- Organizations involved in developing, deploying, or integrating AI foundation models, particularly those operating under FTC jurisdiction, are within scope. This includes companies that license or utilize third-party foundation models, as potential transparency requirements could flow down through AI supply chains. US federal operations and entities engaged in AI risk management are also relevant parties. Primary source →
- What are the key dates for HR 8094?
- No fixed deadline — referred to committee for consideration. Primary source →
- What is the current status of HR 8094?
- As of the last published update, HR 8094 is at the "in committee" stage, with bill status "in committee". Primary source →
- Where can I find the primary source for HR 8094?
- The primary source for the most recent update is at https://www.govinfo.gov/app/details/HOB-2026/HOB-2026-hr8094. AIGI publishes the full citation chain plus every approved brief on this bill. Primary source →
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