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MD HB1477: Consumer Protection - Consumer Reporting Agencies - Use of Algorithmic Systems

Dead/Failed Maryland · 2025 session

Last verified Oct 10, 2026 · official bill text

HB1477 · where it stands

This bill did not become law (dead/failed) · last action Mar 3, 2025.

What do these statuses mean?
Introduced
Filed in the legislature; may be waiting for a committee hearing.
Passed a chamber
Approved by one or both chambers; awaiting further action.
Enacted
Signed into law, or became law without a signature or over a veto.
Effective date
The date the law says it takes effect. Once it has passed, the page shows "Effective" and the date. A court order can still block a law; if we know of one, the summary says so.
Dead / vetoed
Vetoed, failed, or the session ended without action.

In one paragraph

Maryland's HB 1477 (2025) would have added Commercial Law section 14-1228 for consumer reporting agencies that use algorithmic systems to assemble or evaluate consumer information. They would have had to give plain-language explanations, keep a public registry of algorithms, meet error-rate and accuracy thresholds, get quarterly third-party bias audits, and have all automated evaluations reviewed by a human within 24 hours. It was introduced February 7, 2025, its March 4 hearing was canceled, and it died when the session ended.

Plain-English summary, written with AI assistance. The official text controls.

What it means for business

None: the bill died when the 2025 session ended. It would have required consumer reporting agencies using algorithms to add human review, audits and error-rate limits.

Key provisions

  1. Would have applied only to a consumer reporting agency that uses algorithmic systems; each automated evaluation would need a plain-language explanation and human review within 24 hours (expedited review within 48 hours of a request), plus random human review of at least 10% of evaluations
  2. Public registry of all algorithms used (purpose, data sources, general methodology); alternative non-algorithmic assessment for consumers who opt out
  3. Thresholds: overall error rate below 0.5% compared to human review, discriminatory data rates below 0.1%, data input accuracy at least 99.9%
  4. Quarterly harmful bias audits by an independent third party, submitted to the Commissioner of Financial Regulation within 30 days of completion, with annual summaries
  5. Data governance framework with data sources certified at least monthly and minimum data set sizes of 1,000, 5,000 or 10,000 data points depending on algorithm complexity
  6. The Commissioner would set annual assessment thresholds, mandate reviewer training and run a whistleblower program, and could adopt regulations; the bill states no penalties and does not define "algorithmic system"
  7. Would have taken effect October 1, 2025

Latest legislative action

Mar 3, 2025. Hearing canceled

Sponsors

NameRoleDistrict
D Sponsor HD-012

Related and companion bills

Topics and industries

This page is general information, not legal advice. Summaries are AI-assisted and may contain errors; read the statute for the controlling text and talk to a lawyer about your situation. Full disclaimer · Report an error