The Trust Paradox in High-Volume Hiring

*This article was published in the July edition of HR.com 's Talent Excellence Magazine. The full edition can be read here.

Alright talent aficionados, we need to have a talk about the giant algorithmic elephant in the hiring room. If you’re hiring at scale these days, you’re basically married to AI—whether you like it or not. Need to sift through 5,000 applicants in a week? Sorry, your five recruiters aren’t doing that manually—unless you want them to collectively walk off the job. Enter the resume bots. But here’s the kicker: while AI has become essential, almost no one really trusts it — not the people running the hiring, not the people applying for jobs, and sometimes not even the folks selling it.

Welcome to the Trust Paradox. It’s messy, it’s awkward, and it’s defining the future of high-volume hiring.

The Hiring Manager’s Cold Feet: “Is This Shortlist Really Who I Should Interview?”

Let’s start inside the house. Execs love to brag about their cutting-edge AI-powered recruiting stack, but on the ground, the average hiring manager is a bit squeamish. Sure, they’re all using it. Stats say 99% of hiring managers lean on AI in some way. But when asked if AI could truly replicate their own nuanced decision-making? Just 7% say yes. Oof. Why? Because the AI spits out tidy lists without ever telling you why. It’s that classic “black box” problem. Hiring managers are supposed to trust that the algorithm didn’t just auto-reject the next breakout star because their resume didn’t have the right synonyms for “project manager.” This isn’t just paranoia. Harvard Business School found that 88% of employers think their own systems are filtering out qualified candidates. So naturally, managers start second-guessing. They comb through rejects manually, add extra interviews, basically slowing down the very efficiency the AI promised. It’s like buying a self-driving car, then white-knuckling the wheel the entire time.

Meanwhile, Candidates Are Straight-up Freaked Out

If you think managers have trust issues, try being on the other side. This New York Times piece from early July painted a brutally clear picture about what’s happening today.  Jennifer Dunn, 54, applied for a remote job and was met with an automated video interview that analyzed her face, voice, even the background behind her. The system asked about her motivations and salary expectations, but there was no human on the other end. Even though AI had a friendly tone, the conversation “felt hollow,” Ms. Dunn said. In the end, she hung up before finishing the interview. Or look at Emily Robertson-Yeingst, 57, of Centennial, Colo. She was forced to sit through over 12 questions with a hiring AI named Eve, only to be ghosted and later see that job posted again on LinkedIn, leaving her with a sour taste in her mouth and questions many other job seekers are asking themselves today. “Were you just using me to train the A.I. agent? Or is there even a job?” Candidates are wary. Managers are wary. Yet everyone’s doubling down on more tech.

The Big Hypocrisy: We Love Our AI, but Judge Candidates for Using Theirs

Now, for my favorite bit of irony in this whole circus. Companies pour millions into AI to make hiring faster, smarter, “more objective.” They proudly advertise their cutting-edge platforms that parse language models, score candidates on sentiment, and predict who will stay longer based on past data points. But the second a candidate uses AI on their side? Suddenly it’s scandalous.  

• Recruiters will wrinkle their noses and say, “This feels inauthentic — like they didn’t put in the effort.”
• Hiring managers grumble, “Anyone can sound good with a chatbot. I want to know who they really are.”

Meanwhile, the company’s own process is a clinical gauntlet of keyword screens, automated interview scoring, and pattern recognition models. It’s basically an AI arms race — just one where apparently only employers are allowed to bring bots to the battlefield. It’s also deeply human to want it both ways. We love tools that save us time, but we’re suspicious of candidates who look too polished or who admit to using ChatGPT to help tighten their resume bullet points. And let’s be real: in high-volume environments, we want people to make it easier on us. A candidate who uses AI to better articulate their skills or who practices with an AI video coach might actually be more prepared. Yet we often write them off as somehow less genuine. It’s a weird double standard that feeds right into the trust paradox. Companies don’t completely trust their own AI to evaluate humans, but also don’t trust humans who use AI to navigate the process. The result? Everyone second-guesses each other, even as the whole system leans on automation to function.

Why it Matters More Than Ever in High-volume Hiring

Look, in high-volume scenarios like seasonal retail, contact centers, or rapid expansion pushes, you can’t exactly handcraft the hiring experience for every applicant. AI is quite literally the only way to survive. But if hiring managers don’t trust the outputs, they’ll slow everything down. If candidates don’t trust the process, they ghost or blast your company on TikTok for being robotic and soulless. Either way, your carefully built pipeline starts leaking talent. So how do we fix this? Build trust on both sides. Alright, rant over. Let’s get practical. Here’s how to navigate the trust paradox so you’re not starring in your own personal episode of Black Mirror.

1. Make the AI less mysterious. Give hiring managers dashboards that show why a candidate was picked or filtered out: skills, experiences, certifications, whatever’s relevant. Do the same for candidates: if they get rejected, offer a polite, data-backed explanation. Less secrecy means less fear.
2. Keep real human moments. Even if you’re hiring 1,000 warehouse workers, sprinkle in short recruiter calls or quick “hello” video chats. 
3. Reassess what “qualified” means. A lot of these black box problems come down to rigid, outdated requirements. If you tell your AI to only look for Ivy League degrees, don’t be surprised when it screens out that scrappy, high-potential non-traditional candidate. Work with hiring managers to rewrite the criteria.
4. Normalize candidates using AI too. Stop clutching your pearls if someone used ChatGPT to tighten their resume. You’re using machine learning to filter them. They’re using it to look sharp. Fair game.
5. Constantly audit your process. Don’t let your hiring stack operate on blind faith. Run periodic checks: who’s getting screened out, who’s sailing through, and does it match the talent you actually want? Adjust as needed.

In the end, trust isn’t just a feel-good bonus. It’s the critical grease that keeps your high-volume hiring machine from seizing up. If managers don’t trust the tools, they slow it down. If candidates don’t trust the process, they drop out. Want your AI investment to pay off? Build transparency, keep it human, and make trust the priority — not the afterthought. Because until then, we’re all just squinting at each other across the algorithm, wondering who’s fooling who.