Algorithmic Bias Risk Checklist

25 questions to identify hidden bias in your AI HR tools before a regulator or plaintiff does it for you.

20–25 YES: Strong Foundation 12–19 YES: Moderate Risk 0–11 YES: High Exposure

Algorithmic bias in HR tools is one of the fastest-growing sources of employment discrimination liability. Most companies have no idea their AI systems are making biased decisions — until a regulator, auditor, or plaintiff finds out first. Answer honestly. Every NO is a gap that needs immediate attention.

0 of 25 answered
YES Answers 0 / 25
Answer questions to see your risk level
A
AI Tool Inventory & Vendor Accountability
01
Have you identified every AI tool used in hiring, screening, performance management, and compensation decisions?
02
Do you have written documentation from each AI vendor explaining how their algorithms make decisions?
03
Have your AI vendor contracts been reviewed by legal counsel specifically for bias liability clauses?
04
Do your vendor agreements require the vendor to notify you of algorithmic changes that could affect outcomes?
05
Have you verified that your AI vendors conduct their own regular bias testing — and can provide proof?
B
Adverse Impact & Bias Testing
06
Has an independent (non-vendor) algorithmic bias audit been conducted on your AI tools in the past 12 months?
07
Do you have documented adverse impact analysis results showing selection rates by race, gender, and age?
08
Have you tested your ATS or resume screening tool for disparate impact on protected class candidates?
09
Is there a process to regularly re-test AI tools for bias as data sets and algorithms are updated?
10
Have you reviewed your AI tool outputs for patterns that could indicate proxy discrimination (zip code, name, school)?
C
Human Oversight & Decision Controls
11
Is there a documented human review process for all AI-assisted hiring decisions before they are finalized?
12
Are hiring managers trained to recognize and override potentially biased AI recommendations?
13
Do candidates have a way to request human review of decisions made by or with AI tools?
14
Is there a clear escalation path when a hiring manager suspects an AI recommendation may be biased?
15
Are final hiring decisions documented in a way that distinguishes AI recommendations from human judgment?
D
Transparency & Candidate Notification
16
Are candidates notified when AI is being used to screen, score, or rank their applications?
17
Do you provide candidates with meaningful information about how AI influences decisions about them?
18
Is your AI use disclosed in your job postings, applications, or candidate communications?
19
Do candidates in Illinois, New York, or Maryland receive the specific disclosures required by state law?
20
Is there a process for candidates to opt out of AI-driven assessment where legally required?
E
Governance, Policy & Regulatory Alignment
21
Does your organization have a written AI Governance Policy that specifically addresses HR use cases?
22
Has your AI governance policy been reviewed and updated to reflect EU AI Act requirements?
23
Is there a designated person or team responsible for AI bias monitoring and compliance in HR?
24
Are records of AI tool use, audit results, and bias testing maintained and accessible for regulatory review?
25
Has your legal or HR team reviewed EEOC guidance on AI in employment decisions issued in the past 12 months?

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ALS Consulting | AI Governance & HR Compliance | algorithmicbiasaudits@gmail.com

For informational purposes only. Not legal advice.