AI accountability debate: are we anthropomorphizing model failures?

Fortune's commentary challenges a widespread rhetorical pattern in AI discourse: the use of human-like language to describe model malfunctions. Words such as "going rogue" and phrases attributing intentionality or agency to AI systems sound vivid and attention-grabbing, but according to the piece, they conceal the real problem. When AI models produce harmful, inaccurate, or unexpected outputs, those failures are not the result of the model deciding to behave badly; they are the consequence of human choices in how the model was built, trained, tested, and deployed. By framing AI failures as if they were human misbehavior, stakeholders—including developers, companies, and regulators—may evade the scrutiny that should fall on their own practices and decisions. The article makes a case that clarity about accountability is essential. The question of who is responsible when an AI system fails is not abstract; it affects how companies implement safeguards, how regulators set standards, and how harmed users can seek recourse. Adopting language that strips away human agency and deposits it into the model itself may feel like a way to explain what happened, but it actually obscures the human decisions and organizational failures that made the problem possible. A more direct and honest conversation about accountability would focus on the actual people and institutions involved in building, deploying, and overseeing these systems.