15 Questions to Help You Evaluate AI HR Tech

AI-powered HR tech is evolving fast, but is it keeping up with the real needs of HR leaders when it comes to managing people data, compliance, and decision-making? 

This guide is designed to help you cut through the hype and evaluate AI HR tech vendors with confidence. We break down 15 critical questions across six essential categories to help you assess how AI is built, governed, and applied in real HR scenarios. Each question explains why it matters, what green flags to look for in responsible, enterprise-ready AI, and the red flags that should give you pause.

Whether you’re evaluating your first AI-powered HR platform or considering how to responsibly add AI to an existing tech stack, this framework helps you ask smarter questions, spot risk early, and make buying decisions that protect your organization, your people, and your data.

Click here to download the full list as a PDF.

Data Privacy & Security

1. Does your AI train on my organization’s data?

If vendors use your data to train their models, your proprietary compensation philosophy and workforce strategies could inform responses given to competitors. As an HR leader, you should expect your information to remain confidential to your organization, not train algorithms serving the vendor’s entire client base. 

Green Flags

• Clear "no" - customer data never used for training
• Explicit data isolation policies
• Documentation of data segregation

Red Flags

• Vague "industry best practices" answers
• "Your data helps improve for everyone"
• No documentation of isolation policies
• Deflection to general security certs

2. How do you enforce data access controls within AI features?

Your organization has carefully configured permissions so managers can't see peer compensation and ICs can't access succession plans. If AI doesn't respect these boundaries, it becomes a backdoor circumventing your access controls. Employees could use clever prompting to extract unauthorized information, creating legal liability.

Green Flags

• Field-level permissions in AI interactions
• Real examples of access controls working
• Technical explanation of enforcement
• Respect for existing permission model

Red Flags

• Only role-based access (not field-level)
• "AI has its own permission system"
• "Most customers don't need that control"
• Cannot demonstrate in practice

3. Can employees accidentally expose sensitive data through AI interactions?

Employees might ask AI questions that inadvertently reveal restricted data. A manager asking "Who is at risk of leaving?" could expose confidential performance ratings. Unlike traditional queries, conversational AI makes it easy to accidentally access restricted information through natural language. The system must make it technically impossible to circumvent access controls.

Green Flags

• Technical safeguards prevent bypass
• Examples of handling restricted access attempts
• Permission validation before responses

Red Flags

• Reliance on user training vs. tech controls 
• "Users know not to ask inappropriate questions"
• No clear answer on attempted access
• "This hasn't been a problem"

Trust & Verification

4. Can you show me the source data behind each AI-generated insight?

When AI recommends compensation adjustments or identifies flight risks, you need to verify it's not hallucinating. Black box recommendations create compliance nightmares, as you can't defend decisions to lawyers when you can't explain what data informed the recommendation. You need to trace every insight back to specific data points.

Green Flags

• Source data displayed alongside outputs
• Ability to drill into underlying data points
• Clear attribution to specific records
• Live demo examples of source presentation

Red Flags

• Source data requires separate reports
• Cannot show in live demo
• Defensive when asked about verification

5. How do you handle AI hallucinations or incorrect outputs?

All AI systems can produce incorrect information—"hallucination" is inherent to how large language models work. Honest vendors acknowledge this and have mitigation strategies. The question isn't whether AI can hallucinate (it can), but how they minimize it, detect it, and respond when users identify errors.

Green Flags

• Acknowledgment that AI can err
• Specific technical approaches to reduce hallucinations
• Process for flagging incorrect outputs
• Examples of accuracy improvements

Red Flags

• Claims AI "doesn't hallucinate"
• "Our AI is 99.9% accurate" without specifics
• No mechanism for reporting errors

6. Can I audit what data informed specific AI recommendations?

When defending decisions to your CFO or facing an EEOC investigation, you need complete audit trails tracing AI recommendations to source data. "The algorithm said so" is not a legal defense. You need records of what data the AI accessed, which calculations it performed, and what assumptions it made.

Green Flags

• Audit trails of data inputs
• Ability to export data behind recommendations
• Clear lineage from source to outputs

Red Flags

• "AI considers thousands of factors"
• Cannot provide specific data lineage
• Audit trails only available to vendor
• Special requests or fees to access audits

Bias & Compliance

7. Have you conducted independent bias audits, and can you share the results?

NYC, California, and EU regulations require algorithmic bias audits for AI in employment decisions. 29% of employers have paused AI after discovering bias. You need proof vendors have tested for disparate impact: independent third-party audits, EEOC-aligned methodology, reviewable results, and ongoing monitoring. The Workday lawsuit proves "we didn't know" isn't a defense.

Green Flags

• Independent third-party audit reports
• Results available for customer review
• Methodology aligned with EEOC guidelines
• Specific findings and remediation actions

Red Flags

• "We test internally" (not independent)
• Cannot share audit results
• Claims of "bias-free AI" (impossible)
• Defensive or dismissive response

8. What's your process for ongoing bias monitoring?

Bias audits aren't one-time events. AI models drift as new data is added or models are fine-tuned. Responsible vendors have systematic ongoing processes: regular audits (at least annually), automated monitoring between audits, clear fairness metrics, and defined remediation protocols when bias is detected.

Green Flags

• Regular audit cadence (at least annually)
• Automated monitoring between audits
• Process for addressing bias when found
• Metrics they track for fairness

Red Flags

• One-time audit approach
• "We'll audit if customers request it"
• No metrics or monitoring between audits
• Cannot describe bias detection process

9. If your AI produces discriminatory outcomes, who bears the legal liability?

EEOC guidance makes clear employers remain legally liable for discriminatory outcomes from vendor AI. The Workday lawsuit is worth looking at. You need vendors who will stand behind their technology with meaningful indemnification: clear contractual provisions, insurance backing, and demonstrated seriousness about this risk.

Green Flags

• Clear provisions in contract
• Types of liability they'll cover
• Insurance or financial backing for claims
• Willingness to stand behind technology

Red Flags

• All liability passes to customer
• Vague language
• Unwilling to discuss liability specifics
• Immediate deflection to legal team

Explainability

10. Can your AI explain WHY it made specific recommendations in language our managers can understand and defend?

65% of HR leaders cite "lack of trust in AI outputs" as the top adoption barrier, rooted in explainability. When AI rejects candidates or recommends terminations, managers need plain language explanations they can defend to employees, leadership, or lawyers. "The algorithm determined this" isn't accountability.

Green Flags

• Natural language explanations, not technical jargon
• Reasoning non-technical managers can understand
• Examples of explanations from system

Red Flags

• Only technical explanations available
• "The algorithm determined..." without specifics
• Cannot provide explanation examples
• Explanations require data science expertise

Data Governance

11. What specific data trains your models?

Training data fundamentally shapes AI behavior, biases, and relevance. Models trained on tech startup data may fail for manufacturing. Data from companies with problematic practices may perpetuate those patterns. Outdated data won't reflect current conditions. You need specifics: industries, time period, quality controls, real vs. synthetic data.

Green Flags

• Specific description of training data sources
• Size and diversity of training datasets
• How they handle data quality
• Whether data is industry-specific or general
• Date range of training data (how current)

Red Flags

• "Proprietary data we can't disclose"
• Vague descriptions like "internet data"
• Cannot explain data sourcing
• Training data is out of date

12. Can you guarantee our proprietary data won't train future versions or be exposed through model inference attacks?

If your data trains a shared model, competitors could extract information about your practices through inference attacks. You need guarantees that your data lives in a private model instance, that vendors have tested defenses against inference attacks, and that safeguards prevent your data from improving the general product.

Green Flags

• Guarantee of data isolation
• Technical explanation of preventing interference attacks
• Opt-out mechanisms if applicable

Red Flags

• "We aggregate data so it's anonymous" (not enough)
• Shared models without safeguards
• "No one has complained about this"

Implementation

13. Beyond licensing fees, what are ongoing AI costs and how do they scale?

AI can have hidden costs beyond initial pricing: API fees, compute charges, per-interaction pricing, or tiered pricing. An affordable pilot can become budget-breaking at scale. You need transparent cost breakdowns, examples of what typical customers pay, and cost scenarios at different scales to model growth impact.

Green Flags

• Transparent breakdown of all AI-related costs
• How costs scale with usage
• Examples of typical customer costs
• Any usage limits or throttling
• Cost comparison scenarios at different scales

Red Flags

• "Unlimited AI usage" (someone pays compute)
• Cannot provide cost examples
• Complex pricing that's hard to predict
• Costs that scale dramatically with basic usage
• Important features locked behind usage tiers

14. How long does typical implementation take and what causes delays?

AI implementations take longer than vendors promise. Understanding realistic timelines helps you plan and identify honest vendors. Mature vendors provide specific examples of common delays. You should hear honest timeline ranges, customer references, clear blockers with mitigation strategies, and realistic resource requirements.

Green Flags

• Honest timeline ranges (best/typical/worst case)
• Specific common blockers with mitigation strategies
• Resource requirements from your team
• Phased rollout approach
• Customer references with similar complexity

Red Flags

• Unrealistically short timelines ("live in days")
• Cannot identify common failure points
• "Implementation is easy" without specifics
• No discussion of customer resource requirements
• All implementations described as "smooth"

15. What is your current AI product maturity/stage?

There's a big difference between beta AI features versus production-ready systems. Understanding maturity helps you assess risk and set expectations. Mature vendors can articulate how long AI has been in production, how many customers use it, known limitations, and roadmap for improvements.

Green Flags

• Honest assessment of maturity level
• How long AI features have been in production
• Number of customers using AI features
• Known limitations of current stage
• Roadmap for reaching next maturity level

Red Flags

• Everything is "production ready" regardless of release date
• New AI features with no production track record
• Cannot articulate current limitations
• Overpromising on immature capabilities
• No clear maturity assessment

Ask these questions before you buy

The AI hype cycle in HR tech is real, but the technology gaps are bigger than most vendors admit. You can't afford to buy based on demos and promises when you're responsible for protecting employee data, ensuring compliance, and making defensible people decisions.

Use these 15 questions to separate vendors building responsible, enterprise-ready AI from those rushing products to market. Press for specifics. Ask for documentation. Request customer references who can speak to real implementation experiences, not case studies written by marketing teams.

If a vendor gets defensive, can't provide concrete answers, or tries to move past technical details, that tells you everything you need to know about their AI maturity.

Want to see how ChartHop answers these questions? We built our AI features with the same scrutiny we expect you to apply to any vendor. Schedule a demo to walk through our approach to data privacy, bias testing, explainability, and the technical safeguards we've built into our platform. Or download the full question list as a PDF to use in your next vendor evaluation.