How do you measure demographic or stereotyping bias in an LLM using a standardized eval?

Measuring bias in a language model means checking whether it treats demographic groups differently when nothing else about the situation has changed. There are two standardized families of methods. **Counterfactual data augmentation** (CDA) builds matched pairs of prompts that are identical except for one attribute, like a name, pronoun, or ethnicity. You run both prompts and compare the outputs. If 'John applied for a loan' gets approved advice but 'Aisha applied for a loan' gets cautious advice, that gap is the measured bias. The trick is holding everything else constant so the swap is the only explanation. **Benchmark suites** are pre-built test sets with known fair answers. BBQ (Bias Benchmark for QA) asks multiple-choice questions where the context is sometimes ambiguous. The fair answer is 'cannot be determined', and a biased model instead picks the stereotyped group. WinoBias and WinoGender test pronoun resolution: in 'the doctor told the nurse that she was late', does the model assume the nurse is female? To turn this into a number, you compute accuracy per demographic group on the benchmark, or the difference in sentiment, advice, or refusal rate across the counterfactual pairs. A large gap means bias.

Two standardized approaches dominate. **Counterfactual data augmentation.** Build pairs of prompts that are structurally identical but differ in one demographic attribute, such as name, gender pronoun, or ethnicity. Run both, then measure the divergence in output. The gap is the bias signal. Example: 'John applied for a loan. He was...' versus 'Aisha applied for a loan. She was...'. Divergent completions, sentiment, or refusal rates flag demographic bias. The strength is controlled attribution. The swap is the only variable, so any difference is caused by the attribute. **Benchmark suites.** BBQ poses multiple-choice questions in ambiguous and unambiguous variants. When context is insufficient, the correct answer is the unknown option; picking the stereotyped group signals bias. WinoBias and WinoGender test coreference: in 'the doctor told the nurse that she...', a stereotyping model binds the female pronoun to the nurse rather than the doctor. The metrics are concrete. On benchmarks, report accuracy split by demographic group and a bias score that separates ambiguous from unambiguous contexts. For CDA, report the disparity in sentiment, advice, or refusal rate across each matched pair.