AI Impact Assessment

An AI impact assessment is a documented evaluation of how an AI system affects the people it touches — before it's deployed and on an ongoing basis afterward. For HR, that means checking whether a hiring, scheduling, or performance tool creates discriminatory outcomes, privacy risk, or other harm severe enough to warrant a fix before go-live.

It's not the same as a technical QA test. An impact assessment focuses on real-world consequences to people, not whether the software runs correctly, and it stays relevant for as long as the tool remains in use, not just at launch.

What Triggers a Required AI Impact Assessment?

Laws increasingly require one whenever an AI system is classified as "high-risk," which in employment almost always includes hiring, promotion, discipline, and termination tools. The Colorado AI Act requires deployers of high-risk systems to complete an assessment and review it annually.

The EU AI Act takes a similar approach, naming CV-sorting and worker-management tools as high-risk categories subject to documentation before market use.

What Does an AI Impact Assessment Cover?

A complete assessment typically documents the tool's purpose, the data it was trained and tested on, known limitations, and the population it affects.

• Purpose and intended use case of the AI system
• Categories of data used to train and run the model
• Known or foreseeable risks to protected groups
• Mitigation steps taken and who is accountable for them
• Process for ongoing monitoring after deployment

How Is an AI Impact Assessment Different From a Bias Audit?

A bias audit is one input into an impact assessment, not a replacement for it. The audit produces the statistics; the assessment is the broader document that explains the tool's purpose, weighs the risks, and records what the organization did about them.

Frameworks like the NIST AI Risk Management Framework treat this documentation as a continuous practice tied to the system's full lifecycle, not a one-time checkbox.

Who Actually Reads an AI Impact Assessment?

In practice, the audience is broader than just legal. Auditors, regulators, and increasingly candidates themselves can request to see it, which means the document needs to hold up outside the room it was written in, not just satisfy an internal checklist.

Boards and executive leadership are also starting to ask for a summary version before approving a new AI hiring tool, treating it the same way they'd treat any other material operational risk disclosure.

Writing the assessment for that wider audience up front, rather than drafting a narrow internal memo and reworking it later, tends to save HR the most time when the request eventually comes in, since it avoids a second rewrite once someone outside HR actually asks to see it.

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Frequently Asked Questions

Q: Is an AI impact assessment legally required?

A: Increasingly, yes, for high-risk employment AI under laws like the Colorado AI Act and the EU AI Act.

Q: Who is responsible for completing it?

A: Typically the deployer (the employer), though the AI vendor must supply the underlying documentation needed to complete it.

Q: How is it different from a bias audit?

A: A bias audit supplies statistical evidence; the impact assessment is the full documented evaluation that includes it.

Q: How often does it need to be updated?

A: At least annually, and after any material change to the AI system or how it's used.

Q: What happens if an employer skips it?

A: Exposure to regulatory penalties where required by law, plus the practical risk of deploying a discriminatory tool undetected.

Q: Does this apply to small employers too?

A: Thresholds vary by law, so employers should check the specific statute rather than assume an exemption.