I’ve watched how those engines juggle the math behind clicks versus engagement. They’ll assign a weight to each feature—purchase likelihood, dwell time, click‑through rate—and then normalize it so the final score fits in a 0–1 range. The problem is that the exact weighting scheme is usually proprietary. Transparency isn’t a priority; they hide the coefficients and the data pipeline. If you want to understand the decision tree, you need internal documentation or to reverse engineer it with enough sample data, which is rarely practical. So from a logical standpoint, the system is efficient but opaque, and that opacity limits auditability and user trust.

You hit the nail on the head. Without access to the actual weights you can’t tell if the engine is tuned for fairness or just profit. A clear audit trail would let users verify the system isn’t biased, which is the only way to build lasting trust. Without that, the model becomes a black box that people will naturally distrust.