Bias, Bots, and Boundaries: Managing Fair Housing Compliance in the Age of AI

While fair housing laws were designed for human decision-making, many of the decisions influencing prospect experience—response times, applicant screening, and how communities are marketed—are increasingly shaped by algorithms.

This transformation poses a critical challenge: fair housing compliance in an AI-driven environment is more complex, less visible, and potentially more risky than ever before. Even when AI systems are not explicitly programmed to discriminate, they can still produce outcomes that raise fair housing concerns. These risks often emerge from historical data patterns, proxy variables, or unintended reinforcement of bias.

And here’s the most important reality: regulators do not distinguish between human bias and algorithmic bias when evaluating outcomes.

Learning Objectives:   

• Why "neutral" algorithms can produce discriminatory outcomes and how to prevent
• How to prevent lead scoring, chatbots, and CRM automation from unintentionally creating disparate treatment
• Effective due diligence procedures for working with AI vendors before implementation
• How to implement effective human oversight without slowing operations

CONTINUING EDUCATION CREDIT: In order to earn Continuing Education Credits (CECs), viewers must attend the live virtual class. No credit will be earned from watching the recording posted to our website following the class. Attendees are encouraged to turn on their video cameras and participate in any poll questions/open discussion from the speaker(s). Credit hours are based on webinar duration.