Proposed Algorithmic Accountability Act
Description
Algorithmic Accountability Act
- Law/Regulation: Algorithmic Accountability Act (Proposed)
- Jurisdiction: United States (Federal)
- Effective Date: Proposed legislation (not yet enacted)
- Purpose: Require companies to assess their use of artificial intelligence and other automated decision systems for accuracy, fairness, bias, discrimination, privacy, and security
- Detection Tools:
- Related Risks:
- Related Regulations:
- EU AI Act - Similar Assessment Requirements
- US EO on AI - Federal AI Guidance
- CA SB 1047 - State-Level AI Regulation
The Algorithmic Accountability Act is a proposed U.S. federal law intended to increase transparency and fairness in automated decision-making systems. It would require large organizations to conduct impact assessments of their algorithms to identify biases and potential harms, ensuring that AI systems do not perpetuate discrimination or compromise privacy. Although not yet enacted, it has significant implications for the future of algorithmic governance.
Scope & Applicability
The proposed Act targets companies that deploy automated decision systems with significant consumer impact.
- Covered Entities: Large businesses that process personal or sensitive data using automated decision-making systems, especially in high-stakes areas such as finance, employment, and insurance.
- Data Types: Personal, sensitive, and consumer-related data processed by AI and machine learning systems.
- Key Exemptions: Small businesses and systems below defined risk thresholds.
Key Requirements
Organizations would need to implement regular algorithmic impact assessments and develop mitigation strategies for identified risks:
- Conduct Algorithmic Impact Assessments (AIAs) for high-risk systems.
- Develop and document measures to mitigate bias, discrimination, and privacy risks.
- Special Focus Areas:
- Bias and Fairness Audits: Regularly test and review AI systems for disparate impacts.
- Transparency Measures: Provide detailed documentation on how algorithms operate and make decisions.
- Additional Focus: Establish internal governance processes to monitor algorithm performance and ensure compliance with evolving standards.
Impact on LLM/AI Deployments
For LLM and other AI systems, the Act would necessitate a proactive approach to algorithmic governance:
- Risk Assessment: Integrate algorithmic impact assessments into the AI development lifecycle.
- Documentation: Produce model cards or similar documentation detailing AI system design and potential biases.
- Public Accountability: Consider making parts of the assessments publicly available to enhance transparency.
- Security and Observability Considerations:
- Logging: Keep detailed logs of algorithmic decisions and data inputs.
- Periodic Testing: Regularly test AI systems for bias and fairness.
- Access Controls: Secure sensitive data used in assessments.
- Real-Time Monitoring: Deploy tools to continuously monitor AI outputs.
- Compliance Reviews: Schedule internal audits to verify ongoing adherence.
Enforcement & Penalties
While still a proposal, enforcement would likely be managed by the Federal Trade Commission (FTC) or a similar federal body.
- Enforcement Body: Likely the FTC.
- Fines and Penalties:
- Substantial Fines: Significant fines tied to the company's size and the severity of violations.
- Ongoing Sanctions: Continued non-compliance could lead to increased penalties.
- Additional Enforcement Mechanisms: Mandatory public disclosures and corrective action requirements.
- Operational Impacts: Increased compliance costs and potential disruptions during implementation of new governance processes.