Myth: a machine-learning attribution model removes human bias from credit assignment

Myth: a machine-learning attribution model removes human bias from credit assignment

The question: rule-based models bake in arbitrary human assumptions (40/20/40, last-click). An algorithmic or 'AI' attribution model learns credit from data — so it must be the objective, bias-free upgrade, no?

What the model is actually learning: any supervised attribution model is trained to predict conversions from observed exposure data. If that data is confounded — and observational path data always is, by targeting and self-selection — the model learns the confound, often more efficiently than a human would. A flexible learner will happily discover that 'this user saw brand search' is hugely predictive of conversion and assign it massive credit, because brand search is correlated with intent. The algorithm didn't remove the bias; it optimized to fit it. Predictive accuracy on observed conversions is not causal validity, and a model can be excellent at the former while being completely wrong about the latter.

The nuance: more model capacity can make this worse. A richer model captures subtler correlations between high-intent behavior and channel exposure, producing a more confident and more granular credit split that is still anchored to confounded data. 'Data-driven' and 'causal' are different properties; conflating them is the core error.

Bottom line for practitioners: judge an algorithmic attribution model by whether its channel rankings survive experimental validation, not by its training-fit or its sophistication. Use it to generate hypotheses and allocate within trusted channels; use randomized holdouts to decide what's real. An ML model fed observational paths inherits every bias in those paths — sometimes more efficiently than the rule of thumb it replaced.