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 divide, protect consumer privacy, ensure algorithms are fair, and insist that technology builds economic opportunity for communities of color. In this role, Vinhcent helps develop and implement policies to increase broadband affordability and digital inclusion as well as bring transparency and accountability to automated decision systems. Vinhcent also serves on several regulatory boards including the California Privacy Protection Agency. Learn more about the Greenlining Institute.
Data Harvesting and Profiling:
- Ricci v. DeStefano (Brittanica)
- Data Brokers are the Problem (EFF)
- ‘GMA’ Gets Answers: Some Credit Card Companies Financially Profiling Customers (ABC News)
Automated Decisions Systems (Algorithms):
- States’ Automated Systems Are Trapping Citizens in Bureaucratic Nightmares With Their Lives on the Line (Time)
- The Accountability Decisions System Accountability Act (AB13) Explained (Greenlining Institute)
- The Administrative Procedures Act (APA) (EPIC)
Community Control and Consumer Protection:
- Community Control of Police Surveillance (CCOPS) (EFF)
- Ten Questions-And Answers-About the California Consumer Privacy Act (EFF)
Racial Discrimination and Data:
- Investing in Disadvantaged Communities (Greenlining Institute)
- EFF to HUD: Algorithms Are No Excuse for Discrimination (EFF)
Fintech Industry and Advertising IDs
- Behind the One-Way Mirror: A Deep Dive Into the Technology of Corporate Surveillance (EFF)
- Visa Wants to Buy Plaid, and With It, Transaction Data for Millions of People (EFF)
Vinhcent: When you go to the grocery store and you put in your phone number to get those discounts, that’s all getting recorded, right? It’s all getting attached to your name or at least an ID number. Data brokers purchased that from people, they aggregate it, they attach it to your ID, and then they can sell that out. There, there was a website, where you could actually look up a little bit of what folks have on you. And interestingly enough that they had all my credit card purchases, they thought I was a middle-aged woman that loved antiques, ‘cause I was going to TJ Maxx a lot.
Cindy: That’s the voice of Vinhcent Le. He’s a lawyer at the Greenlining Institute, which works to overcome racial, economic, and environmental inequities. He is going to talk with us about how companies collect our data and what they do with it once they have it and how too often that reinforces those very inequities.
Danny: That’s because some companies look at the things we like, who we text and what we subscribe to online to make decisions about what we’ll see next, what prices we’ll pay and what opportunities we have in the future.
Cindy: I’m Cindy Cohn, EFF’s Executive Director.
Danny: And I’m Danny O’Brien. And welcome to How to Fix the Internet, a podcast of the Electronic Frontier Foundation. On this show, we help you to understand the web of technology that’s all around us and explore solutions to build a better digital future.
Cindy: Vinhcent, I am so happy that you could join us today because you’re really in the thick of thinking about this important problem.
Vinhcent: Thanks for having me.
Cindy: So let’s start by laying a little groundwork and talk about how data collection and analysis about us is used by companie
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