When you’re down to a shortlist of candidates, the pressure changes.
You’re no longer asking, “Who meets the requirements?” You’re asking harder questions like:
When used well, AI can help you answer those questions with more consistency and less noise, without taking decisions away from people. In fact, many HR leaders are exploring AI precisely because it can strengthen human judgment, not replace it.
Read more: The role of human judgment in better hiring
Let’s start with a clear boundary: AI doesn’t know who your best candidate is. It can’t see character, context, or long-term potential the way humans can.
What it can do is help you make decisions with better inputs and fewer blind spots.
Final rounds often involve multiple stakeholders, each with their own lens. AI can support structured processes, consistent questions, standardized scoring, shared evaluation criteria. That matters because unstructured interviews are where bias often enters.
Consistency does not remove human insight. It makes it more reliable.
AI can surface trends humans might miss: which competencies correlate with performance, where candidates drop out, or where decisions show uneven outcomes across groups.
That said, AI requires guardrails. If models are trained on biased history or used without oversight, they can amplify existing inequities. Responsible use means auditing for fairness, ensuring transparency, and being clear about what problem the technology is solving.
AI is a tool. Like any tool, it reflects how thoughtfully it is designed and used. Although AI models can minimize balance, they can also amplify it if used naively.
When AI is used as decision support rather than decision-making, it helps teams articulate why someone is a fit. It brings structure to conversations about strengths, development areas, and likely support needs.
That shifts the discussion from “I just like them” to “Here’s what we’re seeing, and here’s how we’ll set them up to succeed.”
Read more: Is bias dominating your decisions?
Human bias is not a character flaw. It’s part of how the brain works. We rely on mental shortcuts, especially under time pressure or when a candidate feels familiar. Even experienced hiring panels are not immune. Confidence can make early impressions feel “obviously right.”
AI can help reduce bias when it is used to:
Importantly, AI does not remove accountability. Humans remain responsible for interpreting results, asking follow-up questions, and making the final decision.
The goal is not “bias-free hiring.” The goal is fair, job-relevant, and defensible hiring, supported by better information.
The McQuaig approach is grounded in a simple principle: fairness improves when people are evaluated against clear, job-relevant criteria that are applied consistently.
That means shifting from “Does this person feel right?” to “Is this person a strong match for what the job requires, and what support will help them thrive?”
McQuaig helps reduce bias by enabling you to:
This structured approach narrows the space for unconscious bias and increases confidence in the decision-making process.
Read more: When it comes to bias, does AI help or hurt?
McQuaig Maven is our AI assistant built on two core inputs:
Here’s why that matters in the final stages: Maven can help translate those results into practical next steps, such as custom interview questions, onboarding insights, and coaching tips tied directly to your benchmark.
And the onboarding benefit is real: when you understand how someone prefers to work, communicate, and handle pressure before day one, you can tailor support early, so the person hits the ground running, and the manager avoids preventable friction.
Next time you’re hiring, consider this “human + AI” workflow:
This keeps decision-making human, while reducing the two biggest hiring risks: inconsistency and unexamined bias.
If you’re exploring AI in hiring and want a grounded, practical view, one that protects fairness and strengthens decision quality, join us on February 26th for our next online event: Human-in-the-Loop HR: Using AI Without Losing Judgment, Fairness, or Accountability.