Algorithmic Bias Explained: How Automated Decision-Making Becomes Automated Discrimination

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Vinhcent Le

Senior Legal Counsel of Tech Equity

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Vinhcent Le

Senior Legal Counsel of Tech Equity

As Senior Legal Counsel of Tech Equity, Vinhcent Le (he/him/his) leads Greenlining’s work to close the digital divide, protect consumer privacy, ensure algorithms are fair and 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. Vinhcent received his J.D. from the University of California, Irvine School of Law and a B.A. in Political Science from the University of California, San Diego. Prior to Greenlining, Vinhcent advocated for clients as a law clerk at the Public Defender’s Office, the Office of Medicare Hearing and Appeals and the Small Business Administration.

Economic Equity Policy Staff

Introduction

Over the last decade, algorithms have replaced decision-makers at all levels of society. Judges, doctors and hiring managers are shifting their responsibilities onto powerful algorithms that promise more data-driven, efficient, accurate and fairer decision-making. However, poorly designed algorithms threaten to amplify systemic racism by reproducing patterns of discrimination and bias that are found in the data algorithms use to learn and make decisions.

The goal of this report is to help advocates and policymakers develop a baseline understanding of algorithmic bias and its impact as it relates to socioeconomic opportunity across multiple sectors. To this end, the report examines biased algorithms in healthcare, at the workplace, within government, in the housing market, in finance, education and in the pricing of goods and services. The report ends by discussing solutions to algorithmic bias, explores the concept of algorithmic greenlining and provides recommendations on how to update our laws to address this growing problem.

What is Algorithmic Bias and Why Does it Matter?

Algorithmic bias occurs when an algorithmic decision creates unfair outcomes that unjustifiably and arbitrarily privilege certain groups over others. This matters because algorithms act as gatekeepers to economic opportunity. Companies and our public institutions use algorithms to decide who gets access to affordable credit, jobs, education, government resources, health care and investment. Addressing algorithmic bias, particularly in critical areas like employment, education, housing and credit, is critical to closing the racial wealth gap.

Recommendations for Fixing Algorithmic Bias

There is a growing body of research outlining the solutions we need to end algorithmic discrimination and build more equitable automated decision systems. This report will provide recommendations on three types of solutions as a starting point:

1. Algorithmic transparency and accountability
2. Race-aware algorithms
3. Algorithmic Greenlining

Vinhcent Le

Senior Legal Counsel of Tech Equity

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