The case for boring models: interpretability as a strategic choice
In regulated settings, a slightly less accurate model you can fully explain often beats a black box you cannot. The arithmetic is less obvious than it sounds.
A confession that sounds strange coming from an AI consultancy: a meaningful share of our engagements end with us recommending a simpler model than the client expected. Gradient-boosted trees instead of a deep network. Sometimes a well-crafted scorecard instead of either. Not because we cannot build the complex thing — because in a regulated environment, the complex thing frequently loses on the metrics that decide whether a model ever reaches production.
The full cost of a black box
Consider the lifecycle arithmetic. A black-box model that improves accuracy by a percentage point over an interpretable alternative sounds like an easy win. Now price the rest: validation takes months longer, because explaining the model’s behavior requires an apparatus of post-hoc techniques that validators must also scrutinize. Adverse action or clinical explanation requirements demand reason codes, which for opaque models are approximations bolted on rather than properties designed in. Monitoring is harder, because when performance drifts you cannot easily see why. And every future audit, for the life of the model, inherits all of this.
In some cases the accuracy gain justifies every bit of that cost. Fraud detection at scale, where a fraction of a percent of precision is real money, is a common example. But the justification should be an explicit calculation, made at design time, with the governance costs on the table — not a default inherited from a leaderboard.
Interpretability is an engineering discipline
It is also worth saying: interpretable does not mean primitive. Modern constrained models — monotonic gradient boosting, generalized additive models, carefully regularized scorecards — close much of the accuracy gap while remaining genuinely explainable, not explainable-with-an-asterisk. Building them well takes skill; the feature engineering carries more weight when the model carries less.
The pattern we keep seeing: teams reach for maximum model complexity as a substitute for domain understanding, then pay the interpretability debt for years. Teams that invest the same effort in features, data quality, and problem framing routinely match the black box with a model a validator can read.
A practical default
Our default position, which we adjust with evidence rather than enthusiasm: start with the most interpretable model that could plausibly meet the performance requirement. Establish that baseline honestly. Then let any move toward complexity justify itself — in accuracy terms and in total governance cost. Half the time, the boring model wins outright. The other half, you now have a documented, defensible rationale for the complex one, which is exactly what your validation team was going to ask for anyway.