Here's what's happening in AI hiring right now. And it's not a glitch. It's a pattern.

A big one just landed. A federal lawsuit against Workday, the HR software giant, is moving forward. Workday's tools help screen job applicants for thousands of employers. Right now, rejected applicants have a window to join the case.

Around the same time, something similar happened at Intuit. The ACLU of Colorado filed a complaint for a woman who is Indigenous and deaf. She says an AI video interview tool called HireVue screened her out of a promotion, despite years of strong performance reviews.

Two companies. Two different tools. One very familiar story.

So why should you care? Here's the number that matters: an estimated 87% of companies now use some form of AI in recruiting.

And study after study keeps finding the same thing: these systems repeat the bias baked into the data they were trained on.

This isn't new, either. Back in 2018, Amazon quietly killed an internal AI recruiting tool. Why? It had learned to downgrade resumes that included the word "women's." It had been trained on a decade of resumes that were overwhelmingly male.

That was seven years ago. We had seven years to fix this. Instead, we automated it, and handed it to nearly every employer in the country.

Here's my honest take. This isn't really a story about "bad AI."

It's a story about who was in the room when these systems got built. Whose resumes trained them. Whose blind spots got coded in as the default setting.

If a hiring algorithm was never stress-tested by someone who knows what bias against a mother, or a career-gap survivor, or a disabled applicant actually looks like, it doesn't know it's doing anything wrong. It just does what it was trained to do, thousands of times a day, with no HR person ever looking a rejected candidate in the eye.

What you can actually do about it

If you think you were screened out by an algorithm, not your actual qualifications, start documenting now. Save the job posting. Note the platform used. Save any assessment scores you got. Write down the timeline.

Law firms are already tracking this. Sanford Heisler Sharp has published plain-language explainers on your rights when algorithmic recruiting is involved, and how race, gender, age, and disability discrimination claims still apply even when "a computer did it."

There are also active efforts to gather this information. Sites like classaction.org are collecting stories from people screened out by tools like HireVue, so attorneys can look for patterns across employers.

You don't need to be a lawyer to start a paper trail. You just need to start one now, while you still remember the details.

Now here's the part I won't soften. This is likely to get worse before it gets better.

Companies are adopting these tools faster than regulators can write rules for them. Industry surveys suggest a large share of employers let AI reject candidates with little to no human review.

Every rejected resume becomes new training data. Researchers call this a feedback loop: a biased model gets fed more biased outcomes, and nobody knows yet where the ceiling on that damage is.

So if your job search has felt like it's black-holing you into silence lately, you're not imagining it. And it's not always about you.

But I refuse to leave you stuck in the problem. The long-term fix isn't just filing complaints after the harm is done, though please do that if it applies to you.

The real fix is women moving into the rooms where these tools get designed, tested, and approved, before they ever touch a real applicant. As engineers. As bias auditors. As the people in procurement meetings asking hard questions. As the policymakers writing the guardrails.

There's real funding for this now. Federal STEM and AI-skills training programs, including newly funded initiatives through the National Science Foundation, exist to build pathways into these exact fields. Look for one in your region or industry, and treat it like the career investment it actually is.

This is the whole point of this newsletter. Women don't just need to survive what AI does to us. We need to be at the table deciding what it's allowed to do in the first place.

A hiring algorithm that quietly filters out women isn't inevitable. It's a design choice nobody stopped to question, made by a team that didn't have enough of the right people asking, "wait, who does this hurt?"