Recently, I was tasked with testing a video interview analysis tool and writing a report on its effectiveness. The tool didn’t just record interviews; it went further. It transcribed voices into text, analyzed body language, and then created a scorecard for each candidate based on how well they fit predefined behavioral patterns. The promise? To “avoid bad hires.” Sounds appealing, right?
But as I went through about 20 real-life demo interviews, I realized something wasn’t sitting right with me. The whole process felt cold, transactional—like there was no room for real human connection. The AI generated reports based on posture, tone, and the exact wording of the candidate’s responses. The final scorecard was meant to help employers make “objective” decisions. But here’s the question: Are we really removing bias, or just shifting it into the hands of an algorithm?
The Double-Edged Sword of AI in Hiring
On the surface, tools like these are marketed as efficient solutions. They transcribe interviews, track every word, and even break down non-verbal cues to help employers understand a candidate’s behavior. The goal is to streamline the process, remove the potential for human bias, and create a more objective evaluation. But the more I tested these tools, the more I started questioning the impact.
AI doesn’t make decisions on its own. It needs to be trained. And the data used to train these systems comes from human biases. The tool “learns” that a particular tone of voice signals confidence, or that certain expressions represent dishonesty. But these generalizations fail to account for the complexity of human behavior. For instance, someone speaking softly might be seen as unsure, when in reality they’re just thoughtful or introverted.
The scorecard generated by the AI is supposed to help employers make decisions, but in reality, it could be filtering out the very candidates who would thrive in the job. We might avoid “bad hires,” sure, but are we also missing out on great ones?
Transcribing Voice: When Data Becomes Dangerous
Here’s where things get particularly tricky. The AI doesn’t just analyze body language; it transcribes every word the candidate says. From there, it picks apart specific language choices and scores the candidate based on how well they align with the algorithm’s ideal responses. The problem? Language is nuanced. What if a candidate uses more informal wording or comes from a background where certain phrases mean different things? The AI doesn’t have the cultural context to understand those subtleties.
By transcribing and analyzing every word, the AI can flag candidates for simply not speaking in the “correct” way. Maybe they hesitate, or use filler words like “um” or “you know.” But does that mean they’re not qualified? Or are we just penalizing them for not fitting a predefined communication mold?
The Candidate Scorecard: Objective or Overly Simplified?
After each interview, the AI spits out a scorecard. This scorecard is supposed to give employers a data-driven assessment of the candidate. But how reliable is it? By simplifying human behavior into a set of metrics, we risk oversimplifying the very qualities that make a candidate special. Sure, one person might score lower because they didn’t use the “right” language or held their hands a certain way. But are those really the traits that define success?
The scorecard doesn’t take into account the most important part of hiring: chemistry. The connection between the interviewer and the candidate, the back-and-forth dynamic, the ability to respond to complex questions under pressure—these are things an AI simply can’t measure. And yet, the scorecard becomes a decision-making tool, leaving employers to trust in the algorithm rather than their own instincts.
What’s at Stake: Declining Candidate Quality
I see a real problem on the horizon. As we lean more on AI to assess candidates, I believe we’re going to see a decline in candidate quality. Why? Because we’re prioritizing metrics over human judgment. When employers rely too heavily on scorecards generated by AI, they miss out on the subtleties that make someone a great fit for the job. They ignore candidates who don’t score high enough on arbitrary metrics like posture or tone.
This isn’t just a theoretical issue. We’re already seeing a decline in candidates who truly stand out. The focus on data means we’re less interested in creativity, adaptability, or even empathy—qualities that are hard to quantify, but vital in the workplace. The candidates who might have once shone in an interview by connecting with the interviewer on a personal level are now lost in the shuffle of AI-generated reports.
The Human Element: What We’re Losing
At its core, the interview process is about connection. It’s about seeing if a candidate fits into your company’s culture, if they have the right personality to collaborate with your team, and if they bring something new to the table. AI can’t capture that. It can analyze tone and transcribe every word, but it can’t tell you whether a candidate is truly passionate, or if they’ll bring the kind of creativity or problem-solving skills your company needs.
We risk losing the human touch in hiring. And in doing so, we’re not just avoiding bad hires—we’re also avoiding great ones. We’re training AI to look for specific traits that might not even be relevant to the job. We’re focusing too much on the “right” behaviors and missing out on the bigger picture.
What’s the Way Forward?
AI tools have their place. They can help with the tedious parts of hiring—like transcribing interviews and tracking data. But when we start relying on them to evaluate candidates based on behavior or generate scorecards, we’re going too far. The human element in hiring is irreplaceable. These tools should assist the process, not define it.
Employers need to be cautious. Don’t let AI make your decisions for you. Use it to support your instincts, but don’t rely on it to replace them. Hiring is about people. And no algorithm can ever replace the connection you feel when you meet the right candidate face-to-face.
What do you think? Is AI enhancing the hiring process, or are we losing something more important in the process?
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