Bias Detection

Bias detection is the practice of actively looking for ways a model treats different groups of people differently — whether by race, gender, religion, nationality, age, or other characteristics. Bias can show up in obvious ways (a model that assumes certain professions belong to one gender) or subtle ones (a model that gives shorter, less complete answers to questions phrased in ways associated with non-native English speakers). Detecting bias requires purposeful evaluation design: you have to specifically look for it, using test cases that probe for differential treatment. For behavior architects, bias detection is both an ethical responsibility and a product quality concern — a biased model is a model that isn’t serving all its users equally, which is a failure of its core purpose.