Latest / The Diversity Career Podcast with Fexingo: Inclusion, Equity, and Underrepresented Professionals / How One Company Used Name-Blind Resumes to Fix Hiring Bias
Transcript
- Lucas: So here's a number that stuck with me: a large professional services firm found that when they removed names from resumes during the initial screening, the callback rate for women went up by 32 percent, and the gap between white candidates and ethnic minority candidates shrank by 16 percentage points. Luna: Sixteen points — that's not a tweak, that's a structural shift. Which firm are we talking about? Lucas: This is Grant Thornton UK. They piloted name-blind recruitment starting in 2016, and by 2018 they had rolled it out across all entry-level and experienced hire roles. What's interesting is that they didn't just announce it and flip a switch — they ran a controlled trial first, with real applicants, and compared the results. Luna: Right, so they had a before and after. That's the kind of data that actually proves the case, not just good intentions. Lucas: Exactly. And the pilot covered over 10,000 applications. They looked at callback rates by gender and ethnicity before the name-blind filter, then again after. The gender lift was the headline, but the ethnicity data was arguably more telling — because the gap didn't disappear entirely, but it shrank materially. Luna: I imagine the pushback from hiring managers was real. 'You're taking away my judgment, I know a good candidate when I see one.' Lucas: That's exactly what happened. And look, hiring managers are trained to trust their gut. But the whole point of the pilot was to show that the gut is often picking up on signals that have nothing to do with job performance — like a name that sounds 'foreign' or a name that sounds female. The firm actually published some of the internal pushback in their transparency reports. Luna: So they made the resistance part of the story. That's rare. Most companies just announce the policy and move on. Lucas: Grant Thornton took a different approach. They acknowledged the skepticism, shared the pilot data openly, and then gave hiring managers a transition period. They didn't force everyone into the new system overnight. And after the numbers were out, the resistance softened — because you can't argue with a 32 percent improvement in gender callback rates if you're trying to hire the best talent. Luna: But I have a question. If you remove the name, the bias just moves to the next stage, right? The interview. The accent, the way someone describes their background. What did they do about that? Lucas: Great question. Grant Thornton acknowledged that name-blind screening is not a silver bullet. They paired it with structured interview questions — so all candidates for a role get the same questions, scored against the same rubric — and they trained interviewers on bias. They also made the interview panel diverse. So the name-blind step was one layer in a multi-layer system. Luna: That makes sense. Because if you only fix the resume stage, the bottleneck just shifts. You end up with a diverse shortlist and a homogeneous hire. Lucas: Right. And Grant Thornton was upfront about that. They said publicly, 'Name-blind screening corrects one distortion. It does not correct all distortions.' That honesty actually built trust with their own people and with applicants. Luna: I want to talk about the mechanism, though. Why does a name carry so much weight on a resume? It's literally a few letters. Lucas: It's not rational, it's associative. Studies have shown that resumes with typically white-sounding names get 50 percent more callbacks than identical resumes with Black-sounding names. That's from the famous Bertrand and Mullainathan study from 2004. And gender bias works similarly — male-named resumes get more callbacks in male-dominated fields, female-named in female-dominated fields. The name becomes a proxy for a whole set of assumptions. Luna: So by removing the name, you force the reviewer to actually read the resume. To look at the skills, the experience, the education. Lucas: Exactly. And what Grant Thornton found is that when you do that, the quality of the shortlist actually improves, not declines. The hiring managers were worried they'd end up with weaker candidates, but the data showed the opposite — because bias was no longer filtering out strong candidates who just happened to have a name that triggered an assumption. Luna: Were there any unintended consequences? Like, did they see a drop in the number of applicants from certain groups because they felt the process was still unfair? Lucas: They didn't report a drop. In fact, application volumes from underrepresented groups increased slightly after the policy was announced — probably because the signal of fairness itself attracted candidates who might have self-selected out before. But one thing they did notice: some hiring managers started using other cues to guess at demographic information — like the name of a university, or a club membership, or even the neighborhood listed. Luna: So the bias just found another channel. That's the hydra problem with bias — cut off one head, two more grow. Lucas: Right. And Grant Thornton responded by expanding the blind screening to include other demographic signals. They removed the name of the university for some roles — not all, because sometimes that's relevant — but they tested it. And they also started using skills-based assessments earlier in the process, which are harder to bias against. Luna: It sounds like they treated this as an iterative experiment, not a one-and-done policy. Lucas: That's exactly the right way to frame it. And for listeners who are thinking about how to bring something like this into their own organization, that iterative mindset is key. Start with a pilot. Measure before and after. Be transparent about the results, even the messy ones. And be ready to adjust. Luna: Which brings me to another point: cost. A name-blind screening system doesn't require fancy software — you can do it with a human removing names from resumes, or with a simple script. It's one of the lowest-cost, highest-impact DEI interventions available. Lucas: Yeah, the marginal cost is essentially zero if you're already doing resume screening. The only investment is the time to set up the process and the willingness to confront the data. And that's where a lot of companies get stuck — not on the mechanics, but on the ego of hiring managers who believe they're objective. Luna: You know, it's funny — we talk about evidence-based management in every other part of business, but hiring is one area where gut feeling still rules. Name-blind screening is a chance to apply the same rigor to talent decisions that we apply to marketing or finance. Lucas: That's a really good way to put it. And Grant Thornton's example shows that when you do apply that rigor, you get better outcomes — for the company and for candidates. The 32 percent lift in women's callback rates wasn't a charity move; it was a talent move. Those were strong candidates who were being overlooked because of a name. Luna: And the 16 point reduction in the ethnicity gap — that's not just good optics, that's a broader talent pool. The kind of thing that shows up in retention and promotion rates a few years down the line. Lucas: Exactly. And that's the real payoff. If you get more diverse people in the door at the early career stage, you're feeding the pipeline for mid-career and leadership. Name-blind screening at the entry point amplifies diversity throughout the entire talent system. Luna: Speaking of amplifying, I have to say — this show is exactly the kind of resource I wish I'd had when I was starting my career. You know, real examples, honest conversations, no sugar-coating. And the fact that it stays ad-free is huge. Lucas: Yeah, that's been important to us from day one. We want this to feel like a conversation, not a commercial. And the only reason we can keep it that way is because listeners who find value in it sometimes choose to support the show directly. Luna: If you're one of those people, there's a very simple way to do it. Just go to buy me a coffee dot com slash fexingo. That's buy me a coffee dot com slash f-e-x-i-n-g-o. No pressure, no perks, just a way to keep these episodes free for everyone. Lucas: And thanks to everyone who's already done that — it genuinely helps us spend more time digging into stories like this one. Now, back to the data. One thing I didn't mention is that Grant Thornton's pilot also tracked what happened to the quality of hires. They looked at performance ratings six months in. Luna: And? Did the name-blind hires perform as well? Lucas: Better, actually. The name-blind cohort had slightly higher performance ratings than the traditional screening cohort. Not by a huge margin, but it was statistically significant. So the bias was filtering out not just diverse candidates, but better-performing candidates. Luna: That's the kind of data that should make every CFO pay attention. Better performance, broader talent pool, negligible cost. It's a no-brainer. Lucas: And yet, adoption remains spotty. According to a 2023 survey by the Chartered Institute of Personnel and Development, only about one in five UK organizations uses name-blind recruitment. So there's still a huge gap between the evidence and the practice. Luna: Why do you think that is? If the data is so clear, why aren't more companies doing it? Lucas: I think it's a combination of inertia, skepticism, and the fear of looking like you were biased in the first place. Some hiring managers feel that adopting name-blind screening is an admission that they were biased before. And that's a hard pill to swallow. But the truth is, everyone has implicit biases — the question is whether you design a system that corrects for them or one that amplifies them. Luna: So the cultural barrier might be bigger than the operational one. Lucas: Absolutely. And that's why Grant Thornton's approach of starting with a pilot and being transparent about the results is so effective. It doesn't require anyone to admit bias upfront. It just says: let's run an experiment and see what happens. The data does the talking. Luna: I think there's also a fear of losing control. Managers like the feeling of reading a resume and making a judgment call. Name-blind screening feels like you're taking away their autonomy. Lucas: Right, but you can frame it as giving them better information. Instead of evaluating a name, they're evaluating actual qualifications. It's not less autonomy — it's more targeted judgment. They still decide who to interview. They just do it without the noise. Luna: So for someone listening who wants to propose name-blind screening at their company, what's the first step? Lucas: Find a champion — ideally someone in HR or a senior leader who's open to evidence. Then design a small pilot. Pick one role or one department. Remove names from resumes for that pilot, but keep the traditional process in place for comparison. Track callback rates by demographic group before and after. And commit to publishing the results internally, no matter what they show. Luna: And if the results don't show a clear improvement? What if the pilot shows no change? Lucas: Then you learn something. Maybe bias wasn't operating at that stage in your organization. Or maybe the sample size was too small. Or maybe you need to pair name-blind screening with other interventions. The point is to treat it as an experiment, not a solution. And Grant Thornton's data gives you a benchmark to compare against. Luna: I think that's a really healthy way to approach DEI more broadly. Not as a set of mandates, but as a series of experiments that you iterate on. Lucas: Exactly. And the beauty of name-blind screening is that it's cheap, quick, and reversible. If it doesn't work, you stop. If it does work, you scale. There's very little downside. Luna: And the upside — a 32 percent increase in callback rates for women, a 16 point reduction in the ethnicity gap — that's a pretty compelling reason to try. Lucas: It really is. And it's not just about fairness. It's about not leaving talent on the table. Every time a hiring manager rejects a resume because of a name-based assumption, they might be passing on their next top performer. And in a competitive talent market, you can't afford that. Luna: So what's the one thing you want listeners to take away from this episode? Lucas: That removing names from resumes is one of the simplest, most evidence-backed steps any organization can take to reduce hiring bias. It's not a complete fix, but it's a powerful start. And the data from Grant Thornton — and other firms that have followed — shows that it works. So if your company isn't doing it, ask why not. Luna: And if you are doing it, share your data. Transparency is what moves the whole field forward. Lucas: Well said. That's our show for today. Next episode, we're looking at how one company created a return to work program specifically for caregivers who had left the workforce — with a twist that I haven't seen anywhere else. Luna: Looking forward to it. See you next time.