SXSW 2024
Fixing Broken Algorithms to Stop Digital Redlining
Description:
Homeownership is the fastest way to accumulate wealth, but banks are 14% more likely to reject minority borrowers than those from privileged groups in mortgage applications.
Redlining has historically kept generations of people of color out of the mortgage market, even 55 years after the Fair Housing Act intended to end housing discrimination. Artificial intelligence can help close the homeownership gap if used responsibly. This panel will explore ways to fix broken algorithms and improve consumer lending outcomes while providing guardrails to ensure AI does not perpetuate discrimination.
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Takeaways
- Many think algorithmic systems are neutral, but they’re not. Biases in algorithms & data sets can result in unfair practices for communities of color.
- Communities of color have historically been left out of the mortgage market, and now machines are replicating traits that amount to digital redlining.
- With the right guardrails and more representative data, algorithms powering AI can reverse the trend and make mortgage lending more equitable.
Speakers
- Lili Gangas, Chief Technology Community Officer, Kapor Center
- Delicia Hand, Director of Financial Fairness Advocacy, Consumer Reports
- Michael Akinwumi, Chief Tech Equity Officer, National Fair Housing Alliance
- Melissa Koide, CEO, FinRegLab
Organizer
Aaron Alberico, Consultant, Consumer Reports
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