If a machine learning (ML) model is illegally discriminatory towards vulnerable and underrepresented populations, can we really say it works? Of course not! That illegal behaviour negates the functionality of the ML model, just as much as overfitting or other typically acknowledged ML "bugs". This talk explores the redefinition of what it means for a model to "work" well enough to deploy and dives into the analogy of software engineering debugging practice to explain current strategies for diagnosing, reporting, addressing and preventing the further development of discriminatory ML models.