Podcast Episode: Algorithms for a Just Future

Episode 107 of EFF’s How to Fix the Internet

Modern life means leaving digital traces wherever we go. But those digital footprints can translate to real-world harms: the websites you visit can impact the mortgage offers, car loans and job options you see advertised. This surveillance-based, algorithmic decision-making can be difficult to see, much less address. These are the complex issues that Vinhcent Le, Legal Counsel for the Greenlining Institute, confronts every day. He has some ideas and examples about how we can turn the tables—and use algorithmic decision-making to help bring more equity, rather than less.  

EFF’s Cindy Cohn and Danny O’Brien joined Vinhcent to discuss our digital privacy and how U.S. laws haven’t kept up with safeguarding our rights when we go online. 

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You can also find the MP3 of this episode on the Internet Archive.

The United States already has laws against redlining, where financial companies engage in discriminatory practices such as preventing people of color from getting home loans. But as Vinhcent points out, we are seeing lots of companies use other data sets—including your zip code and online shopping habits—to make massive assumptions about the type of consumer you are and what interests you have. These groupings, even though they are often inaccurate, are then used to advertise goods and services to you—which can have big implications for the prices you see. 

But, as Vinhcent explains, it doesn’t have to be this way. We can use technology to increase transparency in online services and ultimately support equity.  

In this episode you’ll learn about: 

• Redlining—the pernicious system that denies historically marginalized people access to loans and financial services—and how modern civil rights laws have attempted to ban this practice.
• How the vast amount of our data collected through modern technology, especially browsing the Web, is often used to target consumers for products, and in effect recreates the illegal practice of redlining.
• The weaknesses of the consent-based models for safeguarding consumer privacy, which often mean that people are unknowingly waving away their privacy whenever they agree to a website’s terms of service. 
• How the United States currently has an insufficient patchwork of state laws that guard different types of data, and how a federal privacy law is needed to set a floor for basic privacy protections.
• How we might reimagine machine learning as a tool that actively helps us root out and combat bias in consumer-facing financial services and pricing, rather than exacerbating those problems.
• The importance of transparency in the algorithms that make decisions about our lives.
• How we might create technology to help consumers better understand the government services available to them. 

Vinhcent Le serves as Legal Counsel with the Greenlining Institute’s Economic Equity team. He leads Greenlining’s work to close the digital divi