When AI’s human in the loop really isn’t

Concerns about the risks of AI systems are certain to be met with four words: human in the loop. The discussion may broaden, but the assurance is inevitable. It’s an AI governance phrase that’s become so rote you hear it in every direction and likely have said it yourself. But IT leaders should be wary of vendor or team claims that they’ve built human-in-the-loop systems into AI tools because some of these supposed guardrails are no more than rubber stamps. Some so-called human-in-the-loop systems don’t give employees overseeing the AI tools either the control or the time necessary to fix any problems, some IT experts point out. For human-in-the-loop systems to actually work, employees overseeing AI tools need to have the domain knowledge and context to take the action the AI tool is addressing when the AI isn’t involved, and they need to have the authority to override the AI decision, says Doug Shepherd , head of offensive security at internet services provider Cloudflare. Promises of human-in-the-loop systems give IT leaders comfort, but the underlying process often doesn’t work as advertised, he adds. “If your human in the loop can flag something but can’t actually stop it, that’s not human in the loop, that’s a human adjacent to the loop,” Shepherd says. “That’s performative governance.” Shepherd, speaking at the recent CIO 100 Awards and Conference in Frisco, Texas, encouraged attendees to embrace AI and focus on projects that drive adoption and impact. Organizations that fail to push AI initiatives will be left behind, he suggested, but he also warned that blind adoption, without focusing on meaningful outcomes and guardrails, can lead to huge setbacks. Many organizations reach for human in the loop as an important control, but no one stress tests it, he adds. “It gets projects approved, and too often, it does the political work, but not the risk work,” he says. Darren Kimura , CEO and president at AI integration platform vendor AISquared, agrees that many organizations are deceiving themselves with so-called human-in-the-loop systems. “Most companies that say they have a human in the loop actually have a human watching the loop,” he says. “The person can see the decision and flag a concern, but they cannot stop it, change it, reject it, or escalate it.” IT leaders should ask themselves a handful of questions: Can reviewers halt the actions before they take effect? Can they change the output? Are their overrides recorded and enforced downstream? “If the answer to any of those is no, the human is just monitoring AI,” Kimura says. Too many decisions Another problem with human-in-the-loop systems is the decision fatigue that can set in when employees are asked to review too many AI decisions and end up button mashing instead of thinking about the consequences. The AI reviewer needs the expertise and context to evaluate the recommendation, enough time to do so, and both the authority and technical ability to reject or reverse it, says Eric Billingsley , COO and CTO of AI assurance company TrustScale. But even a qualified and empowered reviewer may gradually stop exercising independent judgment when the AI is consistently right, he notes. “If the system is right 95% of the time, the person’s job becomes waiting for the rare case when it is wrong,” he says. “Humans are not particularly good at sustained vigilance of a highly reliable automated system. Eventually, review becomes confirmation.” A good AI system can create bad human controls, he adds. “When the exceptional case arrives, the reviewer may approve it because the system has trained them, through hundreds of correct recommendations, to trust it,” he says. Billingsley advises IT leaders to evaluate human-in-the-loop systems the same way they monitor other security controls. A control must be monitored, tested, and produce evidence that it is operating as intended, he says. “A log showing that someone clicked ‘approve’ is not enough,” Billingsley adds. “You need evidence that the person had the necessary context, applied independent judgment, and had the authority to override the AI.” Robert Blumofe , EVP and CTO at cloud computing and security vendor Akamai, sees the same problems Billingsley does. Some type of human oversight is preferable to fully autonomous AI, he says, but human in the loop can turn into a mind-numbing exercise. “LLMs produce the correct output just often enough to lull us into a complacent belief that they are more reliable than they really are,” he notes. “After diligently checking the AI output each time and finding no errors, diligence wanes, and human in the loop turns into rote approval.” IT leaders should take the time to figure out what they’re getting into when vendors or their internal teams pitch a human-in-the-loop system, Blumofe says. “It’s incredibly important to understand exactly how the system is designed and when and how the human will interact with the AI,” he adds. Organizations should also explore ways to deploy other technologies as guardrails for AI, instead of turning to unreliable human oversight, Blumofe suggests. “You need non-AI systems in the guardrail role,” he explains. “These technology tools would help to automate testing and validation of AI outputs, flag issues, and have the capability to pause the AI work. This keeps humans out of approval loops, while also helping to reduce risk.” When humans aren’t the right choice Other IT leaders suggest that human-in-the-loop systems aren’t the right solution in every AI use case. When AI is used to flag and mitigate cybersecurity incidents, for example, waiting for a human to approve an action may be too late. “If an endpoint is compromised, you may want the system to isolate it immediately,” says AISquared’s Kimura. “Waiting 20 or 30 minutes for someone to approve that action could allow the attack to spread.” The objective is not to put a human into every AI decision, he adds. “It is to put the right human, with the right context and authority, at the right point in the workflow.”