How does Who and WhoAI prevent Bias in Hiring? 

AI, Algorithms & Bias in Hiring: Building a Better Future with WhoAi

As we work to build WhoAi, our AI-powered hiring platform, we’re not just embracing the power of artificial intelligence—we’re actively confronting its most pressing challenges. One that comes up repeatedly is bias, especially in hiring systems. And as litigation against major HR tech platforms like Workday brings this issue into the spotlight, we believe it’s critical to bring clarity and accountability to what we’re doing, how our technology works, and why it’s different.

Let’s start with the basics—because these terms are often used interchangeably or misunderstood.

What is an Algorithm?

An algorithm is simply a set of rules or steps that a computer follows to solve a problem or make a decision. Think of it like a recipe: if X happens, do Y. Algorithms power everything from social media feeds to credit scores—and yes, hiring platforms.

What’s an Algorithmic Model?

An algorithmic model is when those rules (the algorithm) are applied to data to create predictions or classifications. In hiring, a model might analyze resumes and predict which candidates are likely to succeed based on past hiring data.

What is Inference?

Inference is what happens when an AI system applies its model to new data to make a decision or prediction. For example, after learning from thousands of resumes, the system sees a new candidate and “infers” how likely they are to perform well in a specific role.

What is an Inference Model?

An inference model is the engine that makes those predictions in real time. It’s the part of the AI that’s active when you hit “search” or “submit”—it takes what it’s learned and applies it to new situations.

Why Bias Happens in AI

Bias in AI doesn’t usually come from bad intentions. It comes from patterns in data—especially historical data that reflect real-world inequities. If a company has historically favored certain demographics, an AI model trained on that data might learn to do the same.

That’s at the heart of lawsuits like the one involving Workday. Applicants have alleged that its AI-based Applicant Tracking System unfairly screened out older or disabled job seekers. Whether or not the algorithm was explicitly biased, the way the model was trained—and the lack of transparency in how it operated—raised major red flags.

How WhoAi Addresses Bias in Hiring

We’re building WhoAi differently—because we believe hiring should be objective, equitable, and focused on what actually drives success.

Here’s how:

We Start With a Proven, Human-Centered Method

Our foundation is the Who: The A Method for Hiring—a structured process designed to reduce subjective decision-making and focus on outcomes. This methodology already helps human interviewers reduce bias by asking consistent, experience-based questions and scoring candidates against a Scorecard—a clear definition of success for each role.

We Turn the Method Into a Model

Rather than training on biased historical outcomes, our algorithm is designed to reinforce the structure of the A Method. That means:

• Focusing on specific, role-based outcomes
• Anchoring on observable behaviors and skills
• Removing unstructured “gut feel” from the equation

We Build a Transparent Inference Engine

Our inference model doesn’t just predict success; it explains why. It draws from interview answers and performance history to help both humans and AI evaluate a candidate’s fit—clearly, consistently, and without hidden logic.

We Continuously Audit for Bias

We don’t just “set it and forget it.” WhoAi includes bias monitoring and model interpretability tools that help ensure recommendations stay aligned with ethical and performance-based hiring. If patterns emerge that suggest drift or unintended discrimination, we flag and address them.

Why It Matters

We’re entering an era where AI will touch nearly every step of the hiring journey—from sourcing to screening to interviews. Done right, this offers an incredible opportunity to reduce bias by removing the inconsistencies of human judgment. But done wrong, it risks automating discrimination at scale.

At WhoAi, we believe we can—and must—do better. By anchoring our platform in a time-tested hiring methodology, transparently applying AI, and constantly checking ourselves, we’re building a tool that doesn’t just use AI—it uses it responsibly.

Curious to Learn More?

We’d love to share more about how WhoAi works, the research behind our approach, and how we’re helping organizations hire more effectively—and more fairly. Reach out to schedule a demo or a conversation about AI, hiring, and building teams that perform.