Transcript
Speaker: Welcome back to Disability at Work, where we explore what it really takes to build inclusive, accessible workplaces. I'm your host, Ashley Sims. Today's episode builds on a powerful conversation from our sister podcast, Changing Minds and Changing Lives.
Speaker: where Julie sat down with Beverly Dines, COO of FOXE, founder and chief empowerment officer of Beverlytics Consultancy, to unpack new research from sam Stanford's Institute of Human-Centered AI.
Speaker: Together, they explored how AI hiring tools can reinforce bias and lead to systemic rejection. And Beverly shared practical ways job seekers can break out of the algorithm-driven hiring cycle, especially by building human connections beyond the application process.
Speaker: So today we're taking that conversation one step further, because while job seekers are trying to navigate these systems, employers are facing a different kind of pressure. overwhelmed teams, high application volume, and increasing reliance on AI to keep up.
Speaker: So how do employers get out of the AI hiring loop? And more importantly, how do we bring humans back into the hiring process? Let's start with what we're seeing. AI is now embedded in screening, sourcing, and ranking candidates.
Speaker: In theory, it's meant to improve efficiency and reduce bias. But emerging research tells a different story. The Stanford findings show that AI hiring tools can replicate and even amplify systemic bias. leading to patterns of exclusion that disproportionately impact underrepresented talent. And Beverly shared something that really stuck with me.
Speaker: Many job seekers aren't just applying for jobs anymore. They're trying to perform for an algorithm. And that has led to every resume looking the same and no one being their authentic selves.
Speaker: For job seekers, especially those with disabilities, this creates a frustrating loop. You tailor your resume to match keywords, you try to optimize your experience, and you apply again and again and still get filtered out. Not because you aren't qualified, but because your experience doesn't translate cleanly into what the system expects.
Speaker: In the Changing Minds and Changing Lives episode, Beverly talked about ways to break out of that loop. Things like building direct connections, community referrals, and human entry points into organizations.
Speaker: Because ultimately, algorithms don't hire people. People hire people. But here's the other side of the equation. This isn't just a job seeker problem.
Speaker: HR and talent acquisition teams are under immense pressure. Hundreds, sometimes thousands of applications per role, lean teams with limited time and increasing expectations to move faster and prove results.
Speaker: AI has become a necessity for this overworked corporate function. So how do we use it responsibly and intentionally without losing the human signal?
Speaker: And what does this actually look like in practice? First, it really starts with rethinking what we even mean by qualified, because more AI tools are built to look for very linear keyword matched experience.
Speaker: And the reality is that leaves a lot of people out, especially people with non-traditional career paths, resume gaps because of illness or caregiving or skills they built outside of formal roles.
Speaker: So the shift here is pretty simple, but important. Employers have to step back and ask, are we actually measuring someone's capability or are we just pattern matching against a resume?
Speaker: Because those are not the same thing. And then there's a the tools themselves. Bias doesn't always come from bad intent. It often comes from the data these systems are trained on or how they're designed to make decisions.
Speaker: So there's a real responsibility here to stay close to what your tools are actually doing. And that means looking at rejection patterns, paying attention to whether certain groups are being filtered out at higher rates, and working with vendors who can clearly explain how these models function.
Speaker: Because if you can't explain how a hiring decision is made, that's a risk. Another big piece of this is creating what I call human off-ramps. Not every candidate should be evaluated start to finish by AI.
Speaker: There needs to be a way for people to enter the process outside of that algorithm. That might look like referral pathways, partnerships with community-based organizations, or even just building in a second look process for candidates who were screened out.
Speaker: This is where those community partnerships really matter, because they create a bridge between talent and employers that technology alone can't replicate. And to be clear, this isn't about removing AI from the process altogether. It's about using it differently.
Speaker: AI should be a guide, not a gatekeeper. So instead of using it to automatically reject candidates, think about how it can flag or surface talent for human review.
Speaker: Even small adjustments there can open the door to a much wider, more diverse pool of candidates. And finally, this all comes back to how you approach hiring in the first place.
Speaker: If you're always hiring reactively, AI ends up driving the process and deciding who gets seen. But when you invest in building real talent pipelines, everything shifts.
Speaker: You start creating familiarity, trust, actual relationships that exist outside of a single application. That might mean engaging with talent before a role even opens, or building ongoing relationships with talent networks, like Disability Solutions, for example, which connects employers to one of the world's largest community-based talent networks for the disability community.
Speaker: It also means not just waiting for applications to come to you, but proactively sourcing candidates through resume databases, showing up at career fairs, community events, or networking spaces, the places where real connections happen. And then making sure there are clear, structured pathways into roles so candidates don't have just one shot to be seen.
Speaker: Because when you do all of that, you're not relying on a single system or a single moment to identify talent. You're creating multiple ways in and making it much harder for great candidates to be filtered out before a human ever gets involved. Getting out of the AI loop isn't something one side can fix alone.
Speaker: Job seekers are learning how to navigate systems that weren't designed for them. Employers are trying to manage scale without losing equity. The solution lives in the middle.
Speaker: It's about more transparency in how hiring decisions are made, more access points beyond applications, and more accountability in how technology is used.
Speaker: At the end of the day, hiring has always been about potential. Not perfect resumes, not perfect keyword matches, but potential. And if AI systems are narrowing what potential looks like, we need to step in and widen it again.
Speaker: Because inclusion isn't something technology can automate. It's something we design for, something we choose. If this conversation resonated with you, I highly recommend listening to our latest episode of Changing Minds and Changing Lives featuring Beverly Dines.
Speaker: She shares practical, tactical strategies for how job seekers can navigate and break out of the algorithm-driven hiring landscape and why human connection is still the most powerful signal in the hiring process.
Speaker: And for employers and talent leaders listening, this is your opportunity to rethink not just how you hire, but how your systems shape who gets seen. Thanks for joining us today. We'll see you next time.
