82Your training data was collected with selection bias. How do you detect it and correct for it?▼hardMetaAmazonGoogle1 replies◆ premiumIf labels only exist for the cases you already acted on, the model learns a distorted world: great offline, blind to everyone you never saw. Worse, its own decisions pick the next labels. Here is how to spot it and counter it.Open full answer →
54How do you handle feedback loops and bias in a recommendation system?▼hardNetflixMetaYouTube2 replies◆ premiumA recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. The signal is recognizing the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.Open full answer →
54How do you build a data flywheel from production feedback, and what makes feedback loops go wrong?▼mediumOpenAIMetaNetflix1 replies◆ premiumProduction usage can become your best source of training data, or a self-reinforcing trap. The signal is knowing how to capture clean feedback and how to break the loops that quietly corrupt the model.Open full answer →
