04How do you detect and mitigate bias in an ML model used for consequential decisions?▼hardGoogleMicrosoftAmazon1 repliesunlockedFairness questions catch engineers who treat it as a vibe. What lands is knowing the formal fairness metrics conflict mathematically, that you have to choose one deliberately for the context, and where bias enters the pipeline. Here is the rigorous, honest answer.Open full answer →
60Your hiring model never sees gender, and it still discriminates. How do you find and remove proxy features?▼hardLinkedInWorkdayMeta◆ premiumDropping the protected attribute is the answer most candidates give, and it accomplishes nothing: the attribute is reconstructible from the features you kept. Here is how to actually locate the proxies and what removing them costs you.Open full answer →
