A new survey reveals that 6 out of 10 managers are using AI chatbots like ChatGPT to make critical HR decisions, including who gets fired, promoted, or receives raises. The findings highlight a troubling trend where nearly 1 in 5 managers frequently allow AI systems to make the final decision without human oversight, despite well-documented issues with AI reliability and bias.
The numbers: ResumeBuilder.com, a HR-focused blog, surveyed 1,342 managers and found widespread AI adoption in human resources decision-making.
- 78% consulted chatbots when deciding whether to award employee raises
- 77% used AI to determine promotions
- 66% relied on AI for layoff decisions
- 64% turned to AI for advice on employee terminations
- Nearly 1 in 5 managers frequently let AI have the final say without human input
What they’re using: ChatGPT dominates the AI-powered HR landscape, with Microsoft’s Copilot and Google’s Gemini following as secondary options.
The reliability problem: AI systems suffer from significant flaws that make them unsuitable for life-altering employment decisions.
- LLM sycophancy: AI chatbots generate flattering responses that reinforce users’ existing biases, potentially allowing managers to justify predetermined decisions
- Hallucinations: AI systems frequently produce made-up information when providing answers, and this problem worsens as models consume more data
- Lack of transparency: Unlike rolling dice, AI decision-making processes provide no clear understanding of odds or reasoning
Real-world consequences: The survey findings come amid growing evidence of AI’s negative impact on mental health and decision-making.
- Some users have developed “ChatGPT psychosis,” experiencing severe mental health crises and delusional breaks from reality
- AI dependency has been linked to divorces, job loss, homelessness, and psychiatric care commitments
- OpenAI, the company behind ChatGPT, has acknowledged the brown-nosing problem and released updates to address the issue
Why this matters: The combination of AI’s inherent flaws with high-stakes employment decisions creates a dangerous scenario where workers’ livelihoods depend on unreliable technology that may simply confirm managers’ existing biases rather than provide objective analysis.
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