Answer capsule
When automated decision-making technology uses personal data in a consequential workflow, the people function needs to know which data shaped the result, who can correct an error, and how that correction reaches the live decision path.
What the source establishes
- Colorado Senate Bill 26-189 was signed in May 2026 and repeals and reenacts earlier provisions with requirements for automated decision-making technology used in consequential decisions.
- The Colorado Attorney General says the law applies roles to developers and deployers of ADMT that materially influences a consequential decision.
- The Attorney General's page says consumers may request and correct inaccurate personal data used by ADMT, and the new provisions take effect January 1, 2027.
- Colorado rulemaking is still developing; the page says formal rulemaking details and further opportunities for input will be posted, so the current public record is not a final implementation manual.
Map the data that can change a workforce outcome
The direct CHRO answer is to treat correction as an operating workflow, not a privacy inbox that sits outside recruiting, mobility, performance, or other consequential decisions. Inventory the personal data used by each covered or potentially covered application: candidate records, job history, assessments, skills profiles, attendance, productivity measures, manager inputs, inferred attributes, third-party enrichment, and data derived from prior model outputs. For every field, record the source, date, owner, transformation, confidence or status, and the decision it can influence.
Observe the configured workflow rather than relying only on the platform category. An HR suite can contain administrative search, recommendations, ranking, matching, summarization, and decision-support features with different effects. Name which output reaches a recruiter or manager, whether it filters or orders people, what other information appears beside it, and what action follows. A data-correction route cannot work if the employer does not know which version of a record, feature, model, or rule produced the disputed result.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Design correction to reach the decision, not just the database
A person should be able to identify where to raise an issue without knowing the vendor's terminology or internal architecture. Define the intake channel, identity check, accessibility and language support, response owner, service level, evidence the person can provide, and how status is communicated. Separate a request to access or correct personal data from a challenge to the relevance, interpretation, or use of accurate data. Both may matter, but they require different investigation and authority.
When an error is confirmed, update more than the visible profile. Trace copies, caches, feature stores, exports, downstream applications, model inputs, reports, and pending decisions. Decide whether the workflow must pause, whether a prior outcome needs reconsideration, who can authorize a rerun, and how the corrected path is documented. Preserve the original value, correction, reason, affected decision, reviewer, date, and communication without retaining more sensitive information than the legitimate purpose requires.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Join employer and provider responsibilities before deployment
The Attorney General's summary distinguishes developers and deployers. In an employment setting, a vendor may design the technology while the employer selects the purpose, data, thresholds, integration, reviewers, and consequence. The contract and implementation record should say who can locate a person's data, explain transformations, correct source and derived values, propagate changes, preserve logs, support a challenge, notify the other party about defects, and respond when a model or data source changes.
Test those commitments with a representative error before production. Insert a known incorrect record, follow it through the workflow, submit a correction, and confirm where the change appears and where it does not. Include an external data source, a derived attribute, a ranked list, and an already generated report. The exercise should reveal unresolved ownership, inaccessible vendor evidence, stale copies, and decision timing problems while the organization can still narrow the use or change the design.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Prepare for the effective date without pretending the rules are final
Colorado's page gives a January 1, 2027 effective date and says rulemaking will clarify implementation. CHRO teams can build durable capabilities now: a use inventory, purpose and role classification, personal-data map, notice and challenge channel, correction propagation, decision pause and reconsideration rules, vendor evidence, monitoring, and a dated jurisdiction register. These are reviewable operating artifacts even if a later rule changes a form, threshold, timing detail, or defined term.
Keep legal interpretation separate from product and process testing. Record the enacted text and later amendments, proposed and final rules, official guidance, counsel conclusions, and internal controls as distinct layers. Reopen the assessment when Colorado posts formal rules or the workflow, data, population, provider, or decision influence changes. The source establishes a current state and correction right at a high level; it does not establish that every HR tool is covered or that one generic process satisfies every employment, privacy, disability, labor, or local requirement.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
- Could a person be reidentified?
- What was the stated collection purpose?
- Does the tool materially influence who advances?
- What validated job criteria support the output?
- Which data is job-related and known to employees?
- How are context and accommodations represented?
- Which roles and tasks change?
- How are workers or representatives involved?
The publication supports research and executive decision preparation. It does not provide legal, financial, accounting, employment, clinical, cybersecurity, investment, procurement, or implementation advice.