As AI tools have transformed hiring, AI explainability has become a hot topic among recruiters and hiring managers. The common perception is that explainability is only important for identifying bias and reducing compliance risk in hiring after the fact. While that’s certainly true, it’s far from the most important reason your AI hiring systems need to be explainable.
Ultimately, AI explainability matters because it actively helps you understand who you should hire and why certain candidates are likely to thrive in a role. That speeds up your hiring process and results in dramatically better outcomes. While many recruiters treat AI explainability as a nice-to-have, in reality, it’s an absolute necessity for every modern hiring team.
So, how can you implement AI explainability in your hiring process? In this guide, we’ll dive into what AI explainability really means for hiring and explain how you can transform your hiring process from a black box to a glass box.
What AI explainability really means in hiring
Before exploring why explainable AI matters, it’s worth explaining exactly what the term means and how it works in practice.
Basically, explainable AI is a type of AI model that answers a question and then explains exactly how it reached that conclusion. It’s the AI equivalent of “showing your work.”
In hiring, the question is usually “which of these candidates is the best fit for my open role,” and the AI model's solution is a list of top candidates to consider. Explainability would be the AI detailing the factors it considered when comparing candidates, such as specific skills, work experience, education, diversity metrics, and more.
But truly explainable AI doesn’t just list a bunch of hiring-related factors. Instead, it provides an in-depth look at how each factor was weighted, summarizes why candidates scored well or poorly on specific metrics, and highlights any inferences it made about a candidate’s skills or qualifications based on their work history. These details are essential to understanding why each candidate in your applicant pool did or didn’t make the AI’s shortlist.
Richard Govada Joshua, an IT Project Manager at TEKsystems, recommends an easy test to determine whether an AI hiring system is explainable: “If you can’t explain why a candidate was ranked or scored a particular way to the candidate without contacting the company that made your hiring software [...], then the AI is not explainable.”
How explainable AI differs from generic “ethical AI” claims
You may have seen hiring platforms labeled as using “ethical AI.” While that sounds nice, it’s a promise that’s not typically put into practice.
Saying an AI tool is ethical just means it won’t be biased against candidates based on discriminatory or unfair factors like race, ZIP code, age, and more. However, there’s no way to prove whether that promise is being kept.
That’s where explainable AI comes in. It’s the clear, concrete evidence of why candidates were recommended or rejected, making it possible to check that unfair biases didn’t play a role.
A lot of hiring software providers promise ethical AI – but relatively few deliver explainable AI.
The risks of black box hiring
This all means that using opaque AI hiring tools that don’t have explainability built in – in other words, a black box – is a no-go for hiring teams today. There are too many risks, and the stakes are too high.
First, if you don’t know why an AI is recommending specific candidates, you can’t truly know whether those are the best candidates from your applicant pool. It’s all too easy to advance a slate of candidates who aren’t a great fit for your role, only to discover skills mismatches late in the hiring process or suffer from high employee turnover.
Black box AI also slows down your hiring process. When the AI doesn’t tell you why candidates were recommended, it’s up to you to review each candidate and figure out for yourself what they bring to the table. This highly manual process creates bottlenecks and introduces bias into your process.
Then there’s the compliance risk. You could take software companies word that their AI is ethical – but we wouldn’t recommend it. The financial and reputation costs of unfair hiring practices are simply too high. And even if an AI system truly is unbiased, problems can arise when a candidate – or a lawyer – asks why they were rejected. If you can’t answer because you don’t know what factors the AI evaluated, that could be a serious compliance risk.
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From black box to glass box: How explainable AI transforms your hiring process
Explainable AI addresses the problems with black box hiring in three key ways.
Making hiring better by understanding why candidates succeed
Explainable AI hiring tools offer detailed explanations for why each candidate was sourced or recommended for advancement in your hiring process. They offer tools like:
A scoring framework that assigns numeric ratings to candidates’ skills, work experience, and other factors that the AI evaluated.
A summary explaining what skills, experience, or other qualities a candidate did or didn’t have.
Comparison tables or visualizations that show how candidates stack up on key factors in the AI’s evaluation framework.
Counterfactuals that demonstrate what a candidate would need to improve to be more competitive in the AI’s candidate ratings.
You can dig into each candidate and understand exactly what skills and qualities they’ll bring to your role. For candidates who are passed over, you gain the ability to point to exactly what they’re missing. This is powerful information to help you decide who to advance in your process – and ultimately make the best hire.
Making hiring faster by eliminating manual bottlenecks
Explainable AI tools like TestGorilla evaluate candidates based on verifiable skills, not resumes. That eliminates issues with candidates lying on their resumes and cuts candidates’ names, demographic information, and photos out of the evaluation process, eliminating a major source of bias in hiring.
When you can trust and verify what your AI system is doing, you can make hiring decisions faster. Instead of reviewing every profile that the AI recommends, you can spot-check several and then use the recommendations and comparisons to advance a full slate of candidates. The result is that candidates are compared on a level playing field without potential bias. It’s a fairer process that results in better decision-making around every hire.
Crucially, eliminating these manual bottlenecks enables you to implement a “hybrid” approach to hiring like the one recommended by Taras Demkovych, Co-Founder and COO at Forbytes. He suggests using AI to quickly evaluate formal criteria such as skills and experience, then leaving it to your hiring team to make a final decision based on soft skills, potential, and context. “This approach increases efficiency, preserves the credibility of your hiring process, and meets compliance requirements,” he says.
Making hiring scalable with consistent, fair decision-making
Explainable AI enables consistent, auditable decisions at scale, which is crucial for team alignment. Everyone on your hiring team can see exactly how decisions were made in the past and follow the same practices in the future.
Explainable AI also makes your hiring process defensible at scale. You can create an archive of AI-powered scores, candidate summaries, and all other details your hiring software gives you about both recommended and rejected candidates. This data is critical to defend your company’s hiring practices in the event of questions from candidates or regulators.
Govada Joshua recommends creating a chain of responsibility built around explainability. “Document who is responsible for the model, who will review the model, and how issues will escalate,” he says. That way, there’s a clear and well-documented process to follow when an issue arises.
Practical tips for explainable AI hiring
So, how can you make the jump from traditional black box hiring to explainable AI hiring? It starts with choosing an explainable AI hiring platform like TestGorilla.
But there’s more you can do to streamline your process and make the most of AI explainability. Here are a few practical tips to ensure you’re consistently making the best hire.
Practice data minimalism
More data can help AI models make better recommendations about which candidates are a good fit for your role, but it can also lead to bias. Make sure you only feed the AI job-relevant data about candidates’ skills, work experience, and education. We recommend avoiding resumes altogether. At a minimum, you should strip out names, geographic details, and candidate photos to prevent this data from driving bias during AI resume screening. (This is what we do at TestGorilla in our AI-powered Sourcing tools.)
Human-in-the-loop support
AI should always be used as a tool to assist hiring decisions, not a replacement for experienced hiring managers. Look for AI tools that present explainable recommendations and help you make decisions faster. But leave it up to AI-fluent talent on your hiring team to make final decisions about which applicants to advance and who to hire.
Conduct regular bias audits
While hiring software providers are responsible for checking their models for bias, it’s a good idea to run your own internal bias audits, too. Check whether the distribution of age, race, and gender of AI-recommended candidates matches their prevalence in your overall applicant pool. If it doesn’t, you can dig into the AI’s candidate scores or comparisons to find out what data points are driving this disparity.
Be honest with candidates
Be upfront with candidates that your company uses AI in the hiring process and explain at a high level what the evaluation criteria are (e.g., “we evaluate candidates based on specific technical skills and years of experience.”). This can make the process feel more fair for applicants, improve the overall candidate journey, and boost your company’s reputation among talented candidates.
Empower your hiring team with explainable AI
AI explainability is a necessity for modern hiring teams. It doesn’t just help you defend your hiring decisions – and your reputation – after the fact; it’s crucial for speeding up your process and helping you make better hires who will go the distance.
Make sure the hiring software you use includes detailed explanations about AI recommendations and focuses on skills rather than resumes. In addition, your software should be fully transparent and put humans firmly in charge of the hiring process. TestGorilla ticks all these boxes.
Want to learn more? See TestGorilla in action by booking a free demo, or explore it firsthand with a free plan.
Contributors
Richard Govada Joshua, TEKsystems, IT Project Manager
Taras Demkovych, Forbytes, Co-Founder & Chief Operating Officer
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