We spent several months talking to recruiters about their relationship with AI tools — people working in-house TA teams at growing companies, staffing coordinators at mid-size agencies, and a handful of HR ops managers who had recently gone through the experience of piloting or rolling back an automated screening tool. What we found surprised us, and it's driven a lot of how we think about what Hirefathom should and shouldn't try to do.
The question we kept asking was: if you could change one thing about the AI tools you've used or evaluated, what would it be? We expected accuracy to dominate the answers. It didn't. The most common response, across different role types and company sizes, was some version of: I want to understand why it made the decision it made.
The Accuracy Assumption
Vendors in the HR-tech space have spent years competing on claimed accuracy rates. "Our model achieves X% precision in predicting top performers" is the kind of claim that shows up in sales decks and comparison charts. And there's a reasonable intuition that this is what recruiters care about: does the tool surface the right people?
But when you ask recruiters who actually use these tools daily, accuracy turns out to be almost impossible for them to evaluate in practice. They don't have a ground truth to compare against. They don't know which candidates they didn't screen would have been excellent hires. What they can evaluate — and what they're constantly trying to evaluate — is whether the tool's outputs make sense to them. Whether they can look at a recommendation and understand it well enough to defend it to a hiring manager or a candidate who asks why they weren't advanced.
The absence of explainability doesn't just create a usability problem. It creates a trust problem that accumulates over time. A recruiter who gets ten outputs from a tool and can't explain eight of them starts to treat the tool as a black box that they're responsible for overriding. At that point, the tool is creating work — they're reviewing outputs they don't trust and doing their own parallel review anyway — rather than reducing it.
What "Explainability" Actually Means to Recruiters
Explainability in this context isn't a technical concept. Recruiters aren't asking for model weights or attention maps. What they're asking for is: tell me in plain language why you put this person in the top tier and that person lower down. What specific requirements from the job did each candidate meet? Where did they fall short?
This is a more achievable standard than it might sound, and it's fundamentally different from asking a general-purpose AI model to explain its overall quality assessment of a candidate. If a screening tool is doing its job — matching resume content to stated job requirements — it already has the information needed to produce this explanation. A candidate either has five years of relevant operations management experience or they don't. A candidate either lists the required certification or they don't. These facts are extractable from the resume; making them visible to the recruiter is an output design choice, not a technical constraint.
The difference in recruiter experience between "ranked #3" and "ranked #3 because they meet 7 of 8 stated requirements — missing: current industry experience, though their operations background is closely adjacent" is substantial. The second version gives the recruiter something to work with. They can agree, disagree, and decide whether the gap is disqualifying or not. The first version gives them a number and asks them to trust it.
The Override Problem
A theme that came up repeatedly in these conversations was the recruiter's relationship with override capability. Every experienced recruiter has overridden an automated system's recommendation — moved someone up because they had a gut feel about a resume the system scored lower, or moved someone down because something in the application signaled a mismatch the system didn't catch.
What recruiters told us is that the override experience with most tools they'd used felt like a conflict. The tool didn't want to be overridden. Some systems didn't even surface a clean override path; you had to go around the tool rather than through it. Others would log overrides in ways that felt like they were being second-guessed or that created accountability without supporting judgment.
The recruiter's instinct is not noise. A recruiter who has filled fifty similar roles has calibrated judgment about the specific characteristics that predict success in that function at that company. The question is whether the tool treats that judgment as a feature or a workaround. A tool designed for human-in-the-loop use makes override the expected path, not the exceptional one. The shortlist is a starting point; the recruiter's review is the actual decision process. Overrides should be easy, logged (for audit purposes), and should never feel punitive.
The Accountability Anxiety Is Real
Something worth naming directly: a significant source of the explainability demand among TA professionals right now is anxiety about accountability. The legal environment around automated selection tools has shifted. EEOC guidance has sharpened. Several high-profile enforcement actions and settlements involving employers' use of automated hiring tools have received attention in HR trade publications. Recruiters who are aware of this landscape — and many of the more experienced ones are — are nervous about deploying tools they can't explain.
This is not paranoia. If a candidate files an adverse impact complaint, the employer needs to be able to show that their selection process was based on job-relevant criteria applied consistently. A tool that produces a ranked list without documentation of what it evaluated doesn't help with that defense. A tool that produces a ranked list along with a clear record of which criteria each candidate met or didn't meet — and that log is retained — does.
We're not suggesting that every recruiter is running daily compliance scenarios in their head. Most of the time, explainability matters to them for simpler operational reasons: they want to be able to talk to hiring managers about why specific candidates are in consideration. They want to be able to respond to candidate questions honestly. They want to feel like they understand what the tool is doing well enough to take responsibility for it.
What This Means for How AI Recruiting Tools Should Be Built
The recruiter feedback we've heard is fairly consistent in pointing toward a few design requirements. First: show the criteria, not just the ranking. The job requirements that were used to evaluate candidates should be visible and editable, not locked inside the system's logic. Second: show the match, not just the score. For each candidate, show which requirements they met, partially met, or didn't address — in language the recruiter can use in a conversation. Third: make override the default workflow, not the exception. The recruiter's review should be treated as the final step in a supported process, not as a correction to an automated decision.
None of this requires the AI to be less capable. The best explainable systems are more useful precisely because they surface their reasoning — which lets recruiters identify where the system is miscalibrated, give feedback, and catch cases where the stated job requirements don't actually reflect what the hiring manager needs.
What we heard in those conversations was something practical: recruiters don't distrust AI because they think it's inaccurate. They distrust it because they can't see inside it. That's a solvable problem — and the tools that solve it will be the ones that actually get used, rather than piloted once and quietly abandoned.