Human-in-the-loop isn't a safety net. It's a UX problem with a compliance label.

In a recent article on Dev.to, Dimitris K. argues that the common architectural pattern of 'human-in-the-loop' for AI systems is often misunderstood as a safety net, when it is actually a complex UX and attention-management challenge. The author highlights that treating humans as components with infinite attention leads to failure, as reviewers often succumb to 'automation bias' or fatigue. The piece identifies four key failure modes: routing everything to humans, failing to provide context for reasoning, making overrides difficult, and lacking metrics to monitor reviewer performance. Instead of relying on manual review as a default, the author suggests intelligent routing based on confidence and impact, designing interfaces that surface model reasoning, and treating override logs as critical data for system improvement. Ultimately, the author emphasizes that human-in-the-loop systems must be designed to make human intervention efficient, measurable, and meaningful rather than just a box on a diagram.