Detecting and Mitigating Harmful Bias in an AI System: A PM's Plan

As a product manager at OpenAI, how would you keep an AI system you are responsible for from reinforcing harmful biases? ```hint Name the harm before the fix A biased answer in an open-ended chat looks different from a biased score that ranks people. Work out where this system's outputs reach people, and how you would even notice a biased output in each place. ``` ### Clarifying Questions - Which system is in scope: a general-purpose assistant such as ChatGPT, or the model as used through the API by companies making decisions about people, for example screening job applicants? - Which harms matter most: stereotyped or demeaning content, lower answer quality in some languages and dialects, or unequal outcomes when the output feeds a decision? - Is the system already live, with user reports and usage data to learn from, or still before launch? ### What a Strong Answer Covers - A working definition of harmful bias for this system, split into concrete harm types tied to where its outputs reach people. - How each harm is measured, with results broken down by group, language and use case, and turned into release criteria. - Mitigations at each stage (data, training and behavior guidelines, product defaults, limits on high-stakes uses) and what each costs in helpfulness. - Monitoring after launch: user reports, sampling, red-teaming, and how a confirmed bias is fixed and kept fixed. - How conflicts between fairness, accuracy and refusal rates are decided, and by whom. ### Follow-up Questions - Users notice the assistant assumes surgeons are men and nurses are women when no gender is given. How do you confirm the problem, fix it and prove the fix did not hurt answer quality? - A business customer wants to use the model to rank job applicants. What would you require before allowing it? - A change that lowers one bias metric makes the model refuse more questions about religion. How do you decide whether to ship it?