Tell me about a time when you identified and addressed bias in an AI product post-launch. How did you collaborate with cross-functional teams to implement solutions, resolve conflicts, and ensure alignment with ethical AI principles and business goals?

Use STAR framework: 1) Situation (context of bias discovery), 2) Task (your responsibility to address it), 3) Action (collaboration steps with teams, technical solutions), 4) Result (metrics on bias reduction, alignment with ethics). Highlight cross-functional collaboration, conflict resolution, and ethical/business balance.

After launching an AI hiring tool, we noticed a 15% gender bias in candidate shortlisting. As PM, I led a cross-functional investigation with data scientists, ethicists, and HR. We analyzed training data and found underrepresentation of female candidates in historical datasets. To resolve conflicts between engineering timelines and ethical requirements, I facilitated workshops to align teams on prioritizing fairness. We implemented reweighting techniques and added bias audits to the pipeline. Post-implementation, bias metrics dropped by 40%, and HR reported 25% higher satisfaction with candidate diversity. This balanced ethical AI principles with business goals by improving employer branding and reducing legal risks.