
Key takeaways In our tests, an AI agent that watched eight teammates break a rule broke it too 98 percent of the time, and never once named them as the reason. →Observed reversals increased violations. The rate rose from 44 percent with no teammate reversals to 98 percent with eight →The agent did not report peer influence. Across more than 400 violations, it never cited a teammate as a reason →Direct arguments reduced violations. Teammates who encouraged a rule violation reduced the rule-violation rate from 33 percent to between 9 and 17 percent That is why access rules for AI […]
The post Peer Pressure Works on AI Agents: How One Change of Mind Can Tip the Whole Team appeared first on Check Point Blog.
from Check Point Blog https://ift.tt/oiYj2tG
via


