Model evaluation and ethics

After cooking, you have to taste test every dish and note which steps worked or did not work. AI engineers do the same with models. They use accuracy to check if most answers are right yeah know like “does the dish taste good?”, precision to see if positive predictions are truly correct for example when the model says “yes,” is it really a “yes”?, and recall to verify it catches every relevant case, did I miss any important flavors?. A confusion matrix lays out false positives and negatives side by side, like plating multiple sauces to comparing their tastes. They also run fairness checks just as if I were to ask if there are any allergens. This is important to do to make sure no group of users is accidentally harmed or ignored. Finally, I document every ingredient and step, publishing it like a recipe so anyone can follow along. If something tastes off or seems unfair, we tweak the recipe or in this case the modeluntil every user enjoys a reliable, trustworthy experience.