Imagine applying for a job, only to realize your interview was conducted by a machine. That’s not science fiction—it’s the reality for hundreds of thousands of Australians trying to land casual roles at Kmart. The retail giant, like many others, has swapped traditional resumes for AI-driven interviews, claiming speed, fairness, and efficiency. But beneath the glossy promises lies a thorny question: Are we trading human judgment for algorithmic convenience, and at what cost?
Let’s start with the obvious: AI hiring tools are here to stay. Kmart’s partnership with Sapia.ai is emblematic of a broader shift. Companies are increasingly outsourcing the first cut of recruitment to algorithms, which promise to eliminate biases and streamline processes. Yet, as someone who’s watched the rise of AI in hiring, I can’t help but wonder—what exactly are we optimizing for? Speed? Diversity? Or just the illusion of progress? The answer, it seems, is all of the above, but with a heavy dose of compromise.
The AI interview process at Kmart is designed to feel personal. Candidates are asked open-ended questions, given time to reflect, and receive feedback that sounds almost human. Phrases like "service over self" or "avoid dominating conversations" are meant to resonate. But here’s the catch: this feedback is generic enough to apply to millions of applicants. It’s like getting a horoscope that’s 90% cliché. What makes this particularly fascinating is the contradiction it highlights. An algorithm claims to understand your personality, yet it can’t reconcile the idea of someone who prioritizes others while also risking to dominate a group. That’s not a flaw in the AI—it’s a reflection of the absurdity of trying to quantify human traits with code.
Then there’s the diversity angle. Kmart reports that First Nations people now make up 8.25% of their hires, a jump from 3.2% in the general population. Sapia.ai’s CEO, Barb Hyman, attributes this to the comfort AI interviews provide for marginalized groups. "They might assume they’ll be discriminated against in a face-to-face interview," she says. But here’s where my skepticism kicks in: Is the AI truly reducing bias, or are we just masking systemic issues with a shiny new tool? If the data fed into the algorithm reflects historical biases, the AI will replicate them. The real test isn’t whether the system is fair—it’s whether the data it’s trained on is. And that’s a problem no amount of AI can solve alone.
Another layer of complexity comes from the AI’s ability to detect other AI-generated responses. Sapia.ai claims it can flag AI-generated answers about 92% of the time. But what does that mean for candidates? Are we now in a race where job seekers must out-AI the AI? This feels like a dystopian version of the future where even your creativity is under scrutiny. It’s not just about honesty anymore—it’s about outsmarting a machine that’s constantly learning. And if you fail, you’re not just rejected; you’re labeled as a fraud. That’s a chilling thought, isn’t it?
Yet, for all its flaws, the AI system isn’t entirely without merit. It’s faster, more accessible, and gives feedback—a rarity in traditional hiring. But what’s more important: the speed of hiring or the quality of the people hired? I’ve seen too many companies prioritize efficiency over competence, and this feels like the next step in that trend. The irony is that while AI claims to be unbiased, it’s still making decisions based on data that’s inherently human. That data includes everything from historical hiring practices to cultural norms. So, if the system is trained on a dataset that’s skewed, the results will be skewed too. It’s a paradox that no amount of coding can resolve.
What this really suggests is that we’re in the early stages of a technological arms race in hiring. Companies like Kmart, Woolworths, and Qantas are racing to adopt AI, not because they trust it, but because their competitors are. The result is a system that’s both efficient and deeply flawed. And as we move forward, the question isn’t whether we should use AI in hiring—it’s whether we can afford not to. Because the alternative is a world where the human element is sidelined, and the only thing that matters is how quickly you can pass an algorithm’s test.
In the end, the AI interview is a mirror reflecting our own contradictions. It promises fairness but risks entrenching biases. It claims to be personal but delivers generic feedback. It’s a tool that’s supposed to make hiring easier but ends up complicating it further. The real challenge isn’t the technology—it’s our willingness to question it. Because if we don’t, we’ll keep building systems that look smart but feel hollow, and that’s a future I’d rather avoid.
