Meaningful Human Review Under SB 26-189: What It Requires and How to Implement It

The concept of meaningful human review runs through SB 26-189 like a structural requirement. Consumers have the right to request it. Deployers must provide it. Developers must give deployers enough information to implement it. But the statute does not define exactly what "meaningful" means — creating both flexibility and ambiguity.

What the Statute Says

Section 6-1-1704 grants consumers the right to "meaningful human review by a natural person" when automated decision-making technology is used in a consequential decision. The reviewer must be qualified to evaluate the specific type of decision at issue and must have the authority to override the automated system's recommendation.

What "Meaningful" Requires

Based on the statute's structure and legislative history, meaningful human review requires several elements.

Competence. The reviewer must understand both the decision domain and the AI system's role in the process. A customer service representative rubber-stamping algorithmic outputs is not meaningful review. A loan officer who understands credit risk, the factors the AI system weighs, and the system's known limitations is closer to what the statute contemplates.

Information. The reviewer must have access to the information needed to evaluate the AI system's recommendation independently. This means seeing the input data, the system's output, and the principal factors driving that output — not just a binary approve/deny recommendation.

Authority. The reviewer must have the actual ability to override the system's recommendation. If the review process is designed so that overrides are discouraged, penalized, or practically impossible, the review is not meaningful.

Independence. The reviewer should exercise independent judgment, not simply validate what the algorithm decided. Organizations should track override rates — if the rate is near zero, it suggests the review process may be perfunctory rather than meaningful.

Implementation Framework

Building a meaningful human review process requires five components.

First, define the scope. Identify every consequential decision where ADMT is used. For each, document the AI system's role, the data it processes, and the outputs it generates.

Second, designate qualified reviewers. For each decision type, identify personnel with the domain expertise to evaluate the AI system's recommendations. Provide training on the system's functionality, its known limitations, and the factors it weighs.

Third, build the information pipeline. The reviewer must see the relevant input data, the system's output, the confidence level (if available), the principal factors driving the recommendation, and any flags or anomalies.

Fourth, create the override mechanism. Reviewers must be able to override the system's recommendation through a documented process. The override should be recorded, including the reviewer's reasoning.

Fifth, monitor and audit. Track review volume, override rates, time-to-review, and outcomes. If override rates are extremely low or extremely high, investigate why.