Eliminating Bias in Algorithmic Systems Act of 2026

• Authority: United States Congress
• Parent authority: Federal government
• Jurisdiction: United States
• Collections: U.S. federal laws
• Most recent activity: Proposed
• Most recent activity date: 2026-01-15

Risk subdomain coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.

• Unfair discrimination and misrepresentation
• Unequal performance across groups

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

• Increased inequality and decline in employment
• Governance failure
• Lack of capability or robustness
• Lack of transparency or interpretability

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has strong coverage of discrimination and toxicity risks (subdomains 1.1, 1.3), with explicit focus on algorithmic bias and unfair treatment based on protected characteristics. It also addresses governance structures (6.5) and implicitly touches on inequality (6.2) and system robustness (7.3, 7.4) as they relate to bias and discrimination. Coverage is concentrated in the discrimination/civil rights domain.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

• Public Administration (excl. National Security)

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the sector are described.

• National Security

All other sectors had no coverage in this document.

Sectors Summary

This legislation primarily governs the Public Administration sector, specifically federal government agencies (covered agencies) that use, fund, procure, or oversee algorithmic systems. It indirectly affects all sectors regulated by federal agencies through its requirements for civil rights oversight of algorithms. The legislation is cross-sectoral in its potential impact but directly regulates government operations.

Legislative Status

Hard Law

Legislative Status Summary

This is a proposed federal statute (bill) from the United States Congress that would create binding legal obligations on federal agencies. It uses mandatory language throughout ('shall ensure', 'shall submit', 'shall establish') and would have the force of law once enacted.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: The document is a congressional bill as indicated by the enacting clause, which establishes Congress as the proposing authority for this legislation.

Target

• Entity Types: AI_Governance_Actor, AI_Developer, AI_Deployer
• Summary: The bill applies to 'covered agencies' which are federal government agencies that interact with algorithmic systems in various capacities. These agencies may develop, deploy, or regulate AI systems, making them fit multiple entity types.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: The DOJ Civil Rights Division is given explicit authority to establish and lead the interagency working group. Congressional committees serve as enforcers through oversight and reporting requirements.

Monitor

• Entity Types: AI_Governance_Actor
• Summary: Each covered agency must establish a civil rights office to monitor algorithmic harms. These offices report to congressional committees and participate in the DOJ-led interagency working group, creating a multi-layered monitoring structure.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document focuses primarily on the Deploy and Operate and Monitor stages of the AI lifecycle. It governs how federal agencies use, procure, and oversee algorithmic systems, with strong emphasis on ongoing monitoring for bias and discrimination through civil rights offices and biennial reporting requirements.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

• AI Models
• AI Systems

Technical Scope Summary

The document explicitly defines and covers 'covered algorithms' which include machine learning, natural language processing, and AI techniques. It does not distinguish between different types of AI (frontier, general purpose, task-specific) or mention compute thresholds, foundation models, or open-weight models. The focus is on algorithmic systems broadly defined that could have material impacts on federal programs and rights.