How to Comply With the Colorado AI Act (SB 24-205) for High-Risk AI Systems Used in Hiring Decisions Explained
Colorado’s AI Act (SB 24-205) requires deployers of “high-risk” AI used in hiring to implement risk management, bias testing, notice, and appeal-type processes before key compliance dates beginning in 2026. The law targets automated tools that make or materially influence employment decisions and can create discrimination risk. This guide explains who must comply, what “high-risk” means in hiring, and a practical checklist for attorneys advising employers and vendors in Colorado.
Colorado’s Artificial Intelligence Act (SB 24-205) is one of the first comprehensive state laws to regulate “high-risk” AI systems—especially those used to make or materially influence employment decisions. For employers and HR technology vendors, the practical takeaway is simple: if an AI tool can affect who gets interviewed, hired, promoted, or terminated, Colorado is requiring formal governance controls that look a lot like a compliance program: documented risk assessments, testing for discrimination, notices to individuals, and contractual/operational coordination with vendors.
This article focuses on high-risk AI systems used in hiring and employment decision-making, and how attorneys can help employers, staffing firms, and vendors comply with SB 24-205 while reducing employment-law exposure.
1) What SB 24-205 regulates in the hiring context
SB 24-205 applies to certain AI systems that make, or are a substantial factor in making, “consequential decisions.” Employment decisions—such as hiring, promotion, termination, and other decisions affecting an individual’s livelihood—are the core example of a consequential decision. The law’s compliance obligations attach based on a party’s role:
Deployers are typically employers or staffing firms that use the AI system to make or influence employment decisions.
Developers are usually vendors (or in-house product teams) that build or substantially modify the AI system and provide it to others.
In hiring, SB 24-205 targets AI that can do more than routine administrative work. If the tool materially influences an employment decision—such as ranking applicants, scoring interviews, recommending whom to reject, or generating “fit scores” used by recruiters—it may qualify as high-risk depending on how it is used and integrated into decision-making.
High-risk AI examples in hiring
Tools that can trigger SB 24-205 compliance include:
- Resume screening and ranking systems that score candidates and automatically reject or shortlist.
- Chatbot “pre-screen” interviews that score candidates’ answers and drive pass/fail decisions.
- Video interview analysis tools that evaluate speech patterns, facial movements, or sentiment and generate a selection recommendation.
- Assessment platforms that predict job performance and are used as a gatekeeper for interviews or offers.
- Internal talent marketplace algorithms that recommend promotions or “high potential” designations that substantially influence advancement opportunities.
By contrast, tools that merely help with scheduling, note-taking, drafting job descriptions, or other support functions—without affecting selection outcomes—are less likely to be treated as high-risk. However, attorneys should caution clients that “support” tools can become high-risk if HR policies or workflows effectively convert their outputs into selection criteria.
2) Who must comply: deployers vs. developers
SB 24-205 creates distinct obligations for both sides of the HR tech relationship.
Deployers (employers, staffing companies, and other users)
Deployers generally carry the most visible duties in hiring because they directly interact with applicants and employees. In practice, deployers must build an internal process to:
- Identify high-risk AI in use for hiring and employment decisions.
- Conduct and document risk management steps to mitigate algorithmic discrimination.
- Provide notices to individuals when high-risk AI is used in a consequential decision.
- Offer an avenue to contest or appeal certain decisions and request human review where required.
Developers (vendors and product teams)
Developers must provide information to enable deployer compliance, such as documentation about intended use, limitations, training data considerations, and risk mitigation measures. For vendors selling hiring AI into Colorado, the commercial impact is significant: customers will ask for compliance artifacts and contract commitments.
For in-house developers (e.g., large employers building their own scoring models), the company may be both developer and deployer, increasing the need for a unified governance program.
3) Defining “high-risk” in a hiring workflow: a practical test
Because the statute uses functional concepts like “consequential decisions” and “material influence,” compliance starts with a workflow map. A useful attorney-led approach is to ask three questions:
- Decision linkage: Does the AI output determine eligibility (pass/fail), rank order, or a score used to decide who advances?
- Human reliance: Do recruiters typically follow the AI recommendation? Is deviation rare or discouraged?
- Outcome impact: Would an applicant’s outcome likely change if the AI output changed?
If the answer is “yes” in substance, treat it as high-risk and build compliance around it. This conservative approach reduces enforcement and discrimination litigation risk—especially because plaintiffs’ counsel will focus on evidence that the AI output was a substantial factor in an adverse employment action.
4) Core compliance obligations for deployers using high-risk hiring AI
A. Implement a risk management program focused on algorithmic discrimination
SB 24-205’s centerpiece is preventing “algorithmic discrimination.” In employment, that overlaps with Title VII, the ADA, the ADEA, and Colorado anti-discrimination laws. A compliant program should be documented and repeatable.
What to document:
- The hiring stages where AI is used (screening, interview, selection, promotion).
- The purpose of the AI and what it is not intended to do.
- Identified risks (e.g., disparate impact on protected classes; disability-related barriers; proxy variables like zip code or graduation year).
- Mitigation steps (threshold adjustments, feature removal, human review triggers, accessibility accommodations).
- Monitoring cadence and responsible owner (HR, compliance, legal, or a governance committee).
Concrete example: If a resume-ranking model heavily weights continuous employment, that may disadvantage caregivers or individuals with disability-related gaps. A mitigation could include capping the weight of “gap duration” or requiring human review for candidates near the cutoff.
B. Test and monitor for discrimination risk
Although “bias audit” terminology varies across laws, the practical expectation under SB 24-205 is that deployers must take reasonable steps to evaluate whether the system creates discriminatory outcomes.
Testing options in hiring:
- Adverse impact analysis (e.g., the four-fifths rule as a screening indicator) across selection stages.
- Calibration checks to determine whether similar candidates receive materially different scores due to protected-class proxies.
- Accessibility testing for applicants with disabilities (e.g., whether a timed assessment disadvantages certain conditions without accommodation).
Attorneys should coordinate with industrial-organizational psychologists or data scientists where needed, but maintain privilege strategies carefully. If litigation is likely, consider structuring some evaluations through counsel to preserve confidentiality where appropriate under applicable law.
C. Provide notices to applicants and employees
Deployers must be prepared to inform individuals when a high-risk AI system is used in connection with a consequential decision. In hiring, this typically means applicants should be told, in clear language, that an AI tool is being used and what it does at a high level.
Best practices for notice content:
- That high-risk AI is used in the hiring process (and at which stage).
- The type of data considered (resume content, assessment results, interview responses).
- How the AI output is used (ranking, recommendation, or eligibility determination).
- How to request an accommodation or alternative process (ADA alignment).
- How to request human review or contest the decision, if applicable.
From a risk perspective, align AI notices with existing EEO and accommodation statements to avoid inconsistencies that become exhibit material later.
D. Build a contesting/human review pathway
A recurring issue with AI-based hiring is “black box” rejection—candidates are denied without a meaningful explanation or path to correct errors. SB 24-205 pushes deployers toward procedures that allow individuals to challenge outcomes in certain circumstances and seek human consideration.
Operationalizing this:
- Designate an HR contact for AI-related inquiries.
- Create an intake workflow for “AI decision dispute” requests (e.g., incorrect data, assessment malfunction, accommodation request).
- Define when a recruiter must conduct a documented human re-review (e.g., when an applicant alleges disability-related barriers or data inaccuracies).
- Set response timelines consistent with hiring cycles and record retention policies.
5) Developer obligations: what HR AI vendors must provide
Vendors selling or providing high-risk hiring AI into Colorado should expect customers to demand documentation and contractual assurances enabling compliance. Developers generally should be prepared to provide:
- Intended use and limitations (what the model is designed to do; what it should not be used for).
- Risk mitigation documentation (testing methods, known limitations, and controls).
- Data and model change governance (how updates are rolled out; what happens when the model is retrained).
- Instructions for deployer configuration (e.g., settings that can increase discrimination risk if misused).
Contract tip for attorneys: Add terms that require the vendor to notify the customer of
