From Black Box Myth to Engineered Control
A convenient myth persists in boardrooms: ML systems can't be understood, can't be tested, and can only be "validated" by other ML systems. This belief doesn't just excuse poor execution; it normalises it, converting avoidable engineering failures into accepted "AI unpredictability" with no accountable owner.
Engineering Excellence replaces myth with method. We show how disciplined experimentation makes ML behaviour measurable and improvable, how optimisation can be engineered rather than guessed, and why safety-critical domains, like autonomous vehicles operating under millisecond constraints, demand more rigour, not less. The result is faster progress, predictable performance, and AI that scales because it is engineered.
When ML is treated as unknowable, organisations stop measuring properly and failures get blamed on "AI behaviour" instead of weak method. This article dismantles the black-box mythology and replaces it with engineering discipline: experimental design, representative data, validation, monitoring, and explainability. ML can be audited, tested, and improved, if you run it like engineering.
Most optimisation assumes a fixed search space and hopes sampling will stumble into excellence. ECO rejects that constraint: it constructs structure rather than merely sampling, evolving hyperparameter landscapes through local rules and global selection pressure. The point isn't novelty, it's operational efficiency: robust configurations under tight evaluation budgets, with a discovery process you can analyse, not mystify.
Autonomous vehicles expose the lie that "good enough" ML is acceptable. In life-critical environments, imperfect performance isn't a rounding error, it's a hazard. This article shows why disciplined optimisation and robust engineering practice matter when constraints are real: limited evaluation budgets, multi-objective trade-offs, noisy conditions, and millisecond deadlines. When failure isn't an option, method is the product.
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