Fairness Assurance for an AI-Assisted Lead-Scoring System An SMB Governance Case Study

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This anonymized case study describes a fairness-assurance design for a regional professional-services organization using AI-assisted lead prioritization. It demonstrates a repeatable approach to detecting disparate outcomes, investigating contributing factors, implementing mitigations, and operationalizing ongoing governance. The design treats fairness as a continuing governance responsibility rather than a one-time model-validation exercise. It aligns with the NIST AI Risk Management Framework’s emphasis on governing, mapping, measuring, and managing AI risks, including evaluating and documenting fairness and harmful-bias concerns.