AI Impact Assessment for Hiring: Template & Step-by-Step Guide

When Colorado's AI Act went into effect in February 2026, it introduced a requirement unfamiliar to most U.S. employers: the Algorithmic Impact Assessment. Borrowed from the EU's AI Act and data protection frameworks (GDPR, DPIA), impact assessments force organizations to think before they deploy—to document what an AI system does, what risks it poses, and how those risks will be mitigated.

For AI hiring tools, this means you can't just sign a vendor contract, flip a switch, and start screening candidates. You must first conduct a structured analysis of potential harms, discriminatory impacts, privacy risks, and fairness implications.

This guide walks through the entire process with a practical, adaptable template you can use for your own assessments.

What Is an AI Impact Assessment?

An AI Impact Assessment (also called Algorithmic Impact Assessment, or AIA) is a structured evaluation process designed to identify and document:

• How an AI system works and what decisions it influences
• What data it collects and processes
• Potential risks to individuals (discrimination, privacy, accuracy)
• Mitigation measures to reduce those risks
• Ongoing monitoring and accountability processes

Think of it as a risk management framework—similar to a privacy impact assessment (PIA) or data protection impact assessment (DPIA), but focused on algorithmic harm rather than just data privacy.

Where Impact Assessments Are Required

Colorado AI Act (Mandatory as of Feb 2026)

Colorado requires impact assessments for "high-risk AI systems," which explicitly includes AI used in employment decisions. Employers must complete an impact assessment before deploying an AI hiring tool and update it whenever:

• The AI system is materially modified
• New intended uses are identified
• Annually, at minimum

EU AI Act (If You Hire in the EU)

The EU AI Act classifies employment AI as "high-risk" and requires conformity assessments and ongoing risk management—functionally equivalent to impact assessments.

Best Practice Even Where Not Required

Even in states without explicit impact assessment mandates, conducting one is smart risk management:

• Demonstrates good faith if you're investigated for discrimination
• Forces you to understand how your AI tools actually work
• Identifies issues before they become lawsuits
• Creates documentation showing due diligence

When to Conduct an Impact Assessment

Timing matters:

Before Initial Deployment

Conduct an assessment before you use an AI tool on real candidates. This means during the vendor evaluation phase or during a pilot/testing period.

Before Material Changes

If your vendor updates their algorithm, adds new features, or you expand to a new use case (e.g., using a resume screening tool now also for internal promotions), conduct a new assessment or update the existing one.

Annually (Minimum)

Even if nothing changes, review your assessment at least once a year. AI systems can drift over time, and regulations evolve.

Step-by-Step: Conducting an Impact Assessment

Phase 1: System Description and Purpose

Objective: Document what the AI system is, what it does, and why you're using it.

Questions to answer:

• What is the AI system called?
• Who developed it? (vendor name, version)
• What employment decisions does it support or influence? (screening, ranking, interviewing, assessment)
• What job roles or categories is it used for?
• What business problem is it solving? (efficiency, consistency, quality of hire)

Example response:

System Name: HireVue Video Interview Platform (v9.1)

Vendor: HireVue, Inc.

Employment Decision: Screening candidates for customer service representative positions. The system analyzes recorded video interview responses and generates scores based on communication skills, enthusiasm, and problem-solving ability. Scores are used to rank candidates; top 30% advance to live interviews with hiring managers.

Business Purpose: Reduce time-to-hire by pre-screening large applicant volumes (300-500 applicants per open position). Improve consistency in initial screening.

Phase 2: Data Collection and Processing

Objective: Identify what candidate data the AI collects and how it's processed.

Questions to answer:

• What data does the AI collect? (resume text, video recordings, assessment responses, demographic info)
• How is data collected? (candidate upload, web scraping, third-party data sources)
• What data elements are used in the AI's decision-making? (keywords, speech patterns, facial expressions)
• Is any protected class data used? (race, sex, age, disability)—directly or via proxies?
• How long is data retained?
• Who has access to the data? (internal teams, vendor, subprocessors)

Example response:

Data Collected:

• Video recording of candidate responses (5-10 minutes)
• Audio transcription of spoken responses
• Metadata: timestamp, device type, browser, IP address

Data Used in AI Processing:

• Transcribed text (analyzed for keywords, complexity, sentiment)
• Speech characteristics (pace, tone, filler words)
• Visual data (currently disabled—not analyzed)

Protected Class Data: Not directly collected. However, speech characteristics could serve as proxies for race, national origin, or disability.

Retention: 2 years unless candidate requests earlier deletion

Access: HR team, hiring managers, HireVue (vendor)

Phase 3: Risk Identification

Objective: Identify potential harms and risks to candidates.

Categories of risk to assess:

Discrimination / Disparate Impact

• Could the AI produce different outcomes for protected groups?
• Are there features that could disadvantage certain demographics? (e.g., accent detection, speech patterns)
• Has bias testing been conducted?

Privacy Risks

• Is sensitive personal data being collected beyond what's necessary?
• Could data be re-identified or used for unintended purposes?
• Are there adequate data security measures?

Accuracy and Reliability

• How accurate are the AI's predictions? (Does a high score actually correlate with job success?)
• What's the false positive/false negative rate?
• Could the AI penalize good candidates or advance poor ones?

Transparency and Explainability

• Can the AI explain why it scored a candidate a certain way?
• Do candidates understand how they're being evaluated?
• Can hiring managers understand and challenge AI recommendations?

Disability Accommodation

• Could the AI disadvantage candidates with disabilities? (speech impediments, autism, visual impairments)
• Is there a clear accommodation process?

Example risk identification:

Identified Risks:

1. Discrimination Against Non-Native Speakers:

Risk Level: HIGH
The AI analyzes speech clarity and complexity. Non-native English speakers or candidates with accents may receive lower scores even if their communication is adequate for the job. This could produce disparate impact against Hispanic, Asian, and other national origin groups.

2. Disability Discrimination (Speech):

Risk Level: HIGH
Candidates with speech impediments, hearing impairments affecting speech, or autism spectrum conditions (atypical speech patterns) may be penalized by speech analysis algorithms.

3. Privacy: Excessive Data Retention:

Risk Level: MEDIUM
2-year data retention period may exceed business necessity. Video recordings are sensitive data.

4. Accuracy: Unvalidated Predictive Scoring:

Risk Level: MEDIUM
Vendor has not provided validation studies demonstrating that AI scores correlate with actual job performance for our specific roles. We're using the tool without evidence it predicts success.

Phase 4: Mitigation Strategies

Objective: For each identified risk, define how you'll reduce or eliminate it.

Example mitigations:

Risk 1 Mitigation: Non-Native Speaker Discrimination

• Action 1: Conduct bias audit analyzing selection rates by race/ethnicity and national origin
• Action 2: Request vendor disable accent/speech clarity scoring; retain only content-based analysis
• Action 3: Human review of all AI scores; hiring managers instructed to watch videos and independently assess communication adequacy
• Action 4: Clear disclosure to candidates that accent/language will not be evaluated negatively; focus is on content of responses
• Timeline: Bias audit Q2 2026; vendor configuration changes by March 15; training by March 30

Risk 2 Mitigation: Disability Discrimination

• Action 1: Add accommodation notice to all interview invitations: "If you have a disability that may affect video interview performance, contact [email] to request an alternative format"
• Action 2: Create alternative evaluation process: phone interview or written responses to interview questions
• Action 3: Train HR team to process accommodation requests within 48 hours
• Timeline: Implemented immediately (March 1, 2026)

Risk 3 Mitigation: Data Retention

• Action 1: Reduce retention period from 2 years to 1 year
• Action 2: Implement automated deletion workflow at 1-year mark
• Action 3: Provide easy deletion request process (email link in candidate communications)
• Timeline: Policy change March 2026; automated deletion by April 2026

Risk 4 Mitigation: Validation

• Action 1: Conduct 6-month pilot: track AI scores vs. actual job performance of hired candidates
• Action 2: If no correlation found, discontinue AI scoring and use video platform for recording only
• Timeline: Pilot begins March 2026; evaluation September 2026

Phase 5: Accountability and Monitoring

Objective: Define who's responsible and how you'll track ongoing compliance.

Questions to answer:

• Who owns this impact assessment? (role/person responsible for updates)
• How often will the assessment be reviewed?
• What metrics will you monitor? (selection rates, candidate complaints, accommodation requests)
• What triggers a reassessment? (algorithm updates, new laws, identified issues)

Example accountability framework:

Assessment Owner: Director of HR & Compliance (Jane Smith)

Review Frequency: Quarterly review of metrics; full reassessment annually or upon material changes

Monitoring Metrics:

• Selection rates by demographic group (monthly)
• Candidate complaints related to AI evaluation (real-time)
• Accommodation requests (real-time)
• AI score vs. hiring manager decision agreement rate (quarterly)

Reassessment Triggers:

• Vendor algorithm update
• Bias audit shows disparate impact
• New AI hiring law passes in our jurisdictions
• 3+ related candidate complaints in a quarter

Full Impact Assessment Template

Here's a complete template you can adapt for your organization. Copy this into a document and fill in your specific details:

Integrating Impact Assessments With Other Compliance Activities

Impact assessments shouldn't exist in a vacuum. Integrate them with your broader compliance program:

With Bias Audits

Use impact assessments to plan bias audits. Your risk identification (Phase 3) should inform what demographic categories you analyze in the audit. Conversely, bias audit results feed back into the assessment as evidence of actual discrimination risk.

With Vendor Management

Share relevant sections of your impact assessment with AI vendors. Ask them to help you complete sections on how the AI works, what data it uses, and what validation has been done. Strong vendors will have ready answers.

With Privacy/Data Protection

If you have a Privacy Officer or conduct Data Protection Impact Assessments (DPIAs), coordinate with them. There's significant overlap in the data processing analysis.

With Legal/Compliance

Have legal counsel review completed impact assessments, especially the risk identification and mitigation sections. They can advise on legal adequacy of proposed mitigations.

Common Mistakes in Impact Assessments

Mistake #1: Completing it After Deployment

The assessment is supposed to happen before you use the AI tool. Conducting it retroactively defeats the purpose (identifying risks before they materialize) and may not satisfy regulatory requirements.

Mistake #2: Generic, Check-the-Box Responses

Impact assessments must be specific to your organization and use case. Copying a vendor's generic template without customization is insufficient. Regulators can tell.

Mistake #3: Ignoring Disability Risks

Many assessments focus only on race/sex discrimination and neglect disability risks. AI hiring tools—especially video interview analysis, speech evaluation, and timed assessments—pose significant ADA risks.

Mistake #4: No Follow-Through on Mitigations

Identifying risks and proposing mitigations is pointless if you don't actually implement them. Assign owners, deadlines, and track completion. An assessment with unfulfilled mitigations is evidence of negligence, not diligence.

Mistake #5: One-and-Done

Impact assessments are living documents. Update them when the AI changes, when you discover new risks, when laws evolve, or at least annually. A two-year-old assessment is stale.

How EmployArmor Simplifies Impact Assessments

EmployArmor provides guided impact assessment workflows:

• Automated template generation: We populate assessment templates with data from your AI vendor integrations and your hiring process
• Risk libraries: Pre-identified common risks for each tool type (video interview, resume screening, skills assessment) to jumpstart your analysis
• Mitigation recommendations: Suggested actions for each identified risk, customized to your jurisdictions and resources
• Coordination with bias audits: Impact assessments link to bias audit results automatically
• Review reminders: Alerts when it's time to update your assessment (annually or when triggers occur)

Frequently Asked Questions

Can we use a vendor-provided impact assessment, or must we create our own?

Vendor-provided templates are a good starting point, but you must customize them to your specific use case. Your candidate population, job roles, business context, and risk environment are unique. Generic vendor assessments won't satisfy legal requirements.

Who should conduct the impact assessment—HR, Legal, IT?

Ideally, it's collaborative: HR understands the hiring process, Legal knows compliance obligations, IT/Data understands the technical details. Assign one owner (typically HR or Compliance) but involve all stakeholders.

Do we need separate assessments for each AI tool, or one comprehensive assessment?

Separate assessments for each distinct AI tool. A video interview platform and a resume screening tool work differently, pose different risks, and should be assessed independently. You can create a master document with multiple tool sections, but don't lump everything together.

How long does a typical impact assessment take?

For a single AI tool: 8-20 hours spread across multiple stakeholders. First-time assessments take longer (building the framework). Subsequent assessments or updates are faster (2-5 hours).

Are impact assessments confidential, or must they be published like bias audits?

Currently, impact assessments are not required to be published (unlike NYC bias audits). They're internal risk management documents. However, they may be discoverable in litigation or producible to regulators during investigations. Assume they could become public.

Related Resources

• Complete AI Hiring Compliance Guide 2026
• Colorado AI Act: Employer Guide
• How to Conduct an AI Bias Audit
• 2026 AI Hiring Laws: What Changed