Most companies evaluate AI hiring tools the same way they evaluate any SaaS product: demo, feature comparison, price negotiation. That process will get you a vendor with a good sales team. It won't tell you whether their AI is going to create a discrimination lawsuit in 18 months. The evaluation criteria most companies use are precisely backwards.
---
I've been through enough vendor evaluations — on both sides of the table — to know what the typical process looks like. The procurement team puts together a feature checklist. The vendor does a demo and shows you the dashboard. Legal glances at the Master Service Agreement. IT confirms SSO compatibility. HR signs off. Contract goes through.
None of that process surface the things that actually matter for an AI hiring tool.
What you're buying when you license an AI screening product isn't just software — you're buying a set of decisions about how candidates get ranked and filtered, made by a system you don't fully understand, using data you didn't collect, under liability exposure that is rapidly clarifying. The standard SaaS procurement checklist is not equipped to evaluate that risk.
Here's the framework that should replace it.
Compliance posture comes first. Before you ask about features, ask about the regulatory environments the vendor operates in and how they've responded. Do they comply with NYC Local Law 144's bias audit requirement — and if so, can you see the audit results? Are they prepared for Illinois AIPA notice requirements? Have they documented their approach to California's FEHA automated decision system regulations, which established joint liability between employer and vendor as of October 2025? A vendor who can't answer these questions fluently isn't ready for the current market, regardless of how good their demo looks.
Bias audit transparency is non-negotiable. Ask for the vendor's most recent adverse impact analysis. Not a summary — the analysis itself. What groups were tested? What were the outcome disparities? What remediation was applied? What's the acceptable threshold the vendor uses, and who decided that threshold? If the vendor tells you their AI is "unbiased" without providing data, that's a red flag. Every AI system trained on historical data has biases; the question is whether the vendor has measured them honestly and done something about them.
Data governance determines your liability. Where is candidate data stored? Who can access it? How long is it retained? What happens to the data if you terminate the contract? Under California FEHA regulations, you're jointly liable with your vendor for discriminatory outcomes. That means your vendor's data governance practices are your data governance practices. If they have lax retention policies or share training data across clients in ways that introduce cross-contamination, that's your risk exposure.
Explainability affects your operations. If a candidate asks why they were rejected, can you answer? Under Illinois AIPA, candidates have the right to know that AI was used in the decision. Under California's regulations, there are adverse action notice requirements. Some states are moving toward requiring reasons for rejection. A system that produces accurate outputs but can't explain them is a liability as the regulatory environment continues to tighten.
Contract terms are your last line of defense. The indemnification language in your vendor contract should specify who bears liability for discrimination claims arising from the tool's outputs. Many vendor contracts are written to minimize vendor exposure and maximize employer exposure. Read them. Push back. At minimum, insist on cooperation with audits, transparency obligations, and notification requirements if the vendor discovers bias in their models.
The uncomfortable reality is that most HR buyers are not trained to evaluate these dimensions. They know how to assess UX, integration with existing ATS systems, and per-hire cost. The compliance and governance questions require a different knowledge base — one that most procurement processes don't build in.
The companies that get this right treat AI vendor evaluation as a combined function: HR, legal, and IT at minimum, with outside counsel involved if the tool will touch a significant portion of hiring volume. It's slower. It's more expensive. It's also the process that will keep you out of a Mobley-style collective action.
---
Quick Hits
Questions to Ask Vendors About Bias Audits
Start here: "Can you provide your most recent adverse impact analysis by demographic group?" If the vendor hasn't run one, or won't share it, that's your answer. Follow up with: "What is your retest cadence?" and "What changes have you made to your model based on audit findings?" A vendor who can walk you through their audit methodology with specifics has probably done the work. A vendor who pivots to talking about "fairness by design" hasn't.
Red Flags in Vendor Contracts
Watch for: indemnification clauses that cap the vendor's liability at the contract value (which may be far less than litigation costs), data retention language that doesn't specify destruction timelines, definitions of "discriminatory outcome" that are narrower than applicable law, and arbitration clauses that would prevent you from joining or observing any class action involving the vendor's tools. These aren't theoretical risks anymore.
The Explainability Gap
Most enterprise AI tools operate as black boxes from the candidate's perspective — and increasingly from the employer's perspective as well. As notice and adverse action requirements expand across states, the ability to generate a human-readable explanation of a hiring decision is shifting from a nice-to-have to a compliance requirement. Evaluate whether your current or prospective vendor can generate that explanation. Many can't.
---
The Operator's Take
The AI hiring vendor market has a credibility problem that the market itself hasn't fully reckoned with. Too many vendors are selling on the promise of speed and scale while under-investing in the compliance infrastructure that their customers will need to protect themselves.
The buyer's job is to not be fooled by a good demo. The features that matter — explainability, audit transparency, bias testing rigor, contractual protections — don't show up in a dashboard walkthrough. They show up in documentation requests, reference checks with customers who've been through audits, and legal review of the contract terms.
The scorecard approach is the right one: evaluate every vendor on the same dimensions, in writing, before the negotiation starts. It changes the dynamic entirely. Vendors who've done the work welcome the rigor. Vendors who haven't start making promises they can't keep. That gap is exactly the information you need.
---
The AI Screening Vendor Evaluation Scorecard gives you a structured, fillable framework to evaluate every vendor on compliance, bias testing, explainability, data governance, and contract terms — not just features and price. Use it before your next vendor conversation.
Get it here → AI Screening Vendor Evaluation Scorecard
---