The process of using highly-vetted human feedback to train AI is often referred to as the “gold standard” for creating safe and reliable models. However, an inside look at the process reveals that this standard is, in many cases, fool’s gold—a shiny exterior that lacks real substance and value due to the compromised conditions under which the feedback is generated.
The “gold” is supposed to be the expert human judgment that refines the AI. But what happens when that judgment is rushed? A 15-minute review of a complex topic is not an expert consultation; it’s a superficial glance. The feedback collected under such conditions is, at best, a gold-plated approximation of real expertise.
What happens when the “expert” is not an expert at all? When a literature major is forced to rate a response about astrophysics, the feedback is not gold; it’s a guess. The system is systematically mixing these guesses in with real expert feedback, debasing the entire dataset and creating a product that is deceptively shiny but fundamentally brittle.
The industry holds up this human feedback process as proof of its commitment to quality. But the reality is that they are following the letter of the “gold standard” while violating its spirit. By prioritizing speed and cost-cutting over the integrity of the feedback process, they are creating a product that looks valuable on the surface but is ultimately worthless in a crisis.