Many of the technologies used in housing and financial services are known to discriminate against women, people of color, those new to credit, and people with thin credit files. For example, a 2020 publication from The Markup shows that each year, Black and Latino borrowers pay $765 million more than they should in interest costs due to discriminatory statistical or Machine Learning mortgage pricing algorithms. Transparency, accountability, explainability, and fairness are core principles required to mitigate or eliminate algorithmic discrimination in housing. This panel will discuss best practices to make technological advances in AI and machine learning that maximize their benefits to both consumers and corporations while preventing and eliminating injuries to consumers and our greater society.