AI in Hiring: Can You Explain Why a Candidate Was Rejected?

Let’s be honest. Wouldn’t it be easier to let AI handle all of your hiring? Upload the job description, grab a cup of coffee, and come back to a perfect shortlist. It sure can sound that way. But hiring has never been just a matching exercise. Some of the best hires happen because of a great conversation, a shared laugh in an interview, or a hiring manager who noticed something on a resume that no keyword search would ever flag. When software does all the narrowing, a lot of that chemistry, and a lot of amazing talent, can get lost along the way.

This raises a practical question for recruiting teams and hiring managers: if technology helped decide which candidates moved forward, could you explain why a particular candidate did not?

California Is Paying Attention

Questions about automated hiring tools are already playing out in court. In Mobley v. Workday, a federal court in California allowed an age discrimination claim involving AI applicant screening to proceed as a nationwide collective action. Workday stated in filings that 1.1 billion applications were rejected using its software tools during the relevant period. The case is still ongoing, and allegations are not findings. Still, it’s a reminder that “the software made the recommendation” doesn’t end the conversation.

In California, the attention goes beyond the courtroom. Regulations from the California Civil Rights Council on automated decision systems took effect October 1, 2025. They confirm that existing anti-discrimination protections apply when employers use AI and other automated tools in recruiting, hiring, and promotion. They also require employers to keep employment records, including automated decision data, for at least four years. For recruiting teams, the takeaway is practical: understand how your tools influence decisions, and keep the records that help explain the process.

The 235 Candidates Nobody Talked To

Picture 250 applications for one opening. An automated recruiting tool recommends 15, the hiring manager interviews five, and one person gets the offer. Everyone can explain why that person was chosen. Far fewer people can explain what happened to the other 235.

Now, we get it. When a posting brings in hundreds of resumes, nobody is reading every single one, and that’s okay. Sometimes you find a great fit in the first five conversations and make a decision. That happens, and it’s easy to stand behind because people on your team reviewed those candidates and made the call. The concern is when a tool quietly decides who never gets a look, and no one can say why.

Many recruiting platforms already screen resumes, rank applicants, and recommend who gets reviewed, often in the background. A shorter list feels productive, but speed alone says little about whether great people were overlooked. Was the system weighing keywords? Job titles? Years of experience? A degree the role doesn’t actually need? If no one on your team knows, it’s worth finding out before a ranking decides who gets a call.

Here’s the one that stings a little. Think about the candidate who wasn’t quite right for this opening but would have been a terrific fit for a different role on your team. If someone had taken fifteen minutes to speak with them and learn a bit about who they are, you might have found your next great hire. An algorithm ranking applicants against a single job description is not going to tell you that.

Five Questions Worth Asking About Your Recruiting Technology

You don’t need to become a software engineer, but it’s important to understand the tools influencing your hiring. A few questions to consider:

1. What information does the system use to evaluate candidates?
2. How does it decide who is recommended, ranked, or screened out?
3. Are those criteria tied to the real requirements of the role?
4. Do your recruiting team and hiring managers review candidates beyond the system’s recommended list?
5. If a candidate asked why they were passed over, could you explain the process?

Unclear answers don’t mean something is wrong. They do suggest it’s time for a closer look before relying on the tool to screen people out.

Start With the Job Description

Automation amplifies whatever it’s given. An outdated or overly restrictive job description can screen out qualified people at scale before anyone considers what they could contribute. Before launching a search, it’s worth taking a fresh look at what the role truly requires. Is the four-year degree essential, or would equivalent experience do the job? Can the software skills be learned in the first few weeks? Could experience from another industry transfer well?Keep People at the Center

Technology can be a big help with the administrative side of recruiting, like scheduling, organizing applications, and keeping communication moving. Where it is imperative to keep humans involved is in deciding who deserves a closer look. People can ask follow-up questions, recognize transferable skills, appreciate an unconventional career path, and sense whether someone will click with the team. That review matters most when it goes beyond approving a list the system has already narrowed. Looking outside the recommended group now and then can be eye-opening.

Finding Talent Takes More Than an Algorithm

AI can support the process. People make the difference, and the decisions.

At Dynamic, our team looks beyond keywords and rankings to understand the role, the team, and the experience behind each resume, so you get a thoughtful shortlist built around what you actually need. Call Dynamic at 888‑303‑5627 to talk about your next hire.

Dynamic is a certified diverse business, including WBENC certified WBE, WOSB, and California DGS certified Small Business Micro (SB/MB).