TPM Cafe: Opinion

Big Data Is A Civil Rights Issue

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The White House is currently conducting a 90-day examination of the impact of big data on how we work and live. Big data can and should bring greater safety, economic opportunity, and convenience to all people. But there are risks. The concerns of the business and technology sectors are well-established and it’s imperative that the civil rights community also has a seat at the table. Without our voices, the stakes for communities of color, individuals who speak languages other than English, people with disabilities, and low-income populations will not be adequately addressed.

That’s why we’ve joined with our colleagues in the women’s, Latino, Asian-American, and African-American communities to voice the needs of our constituents, share our priorities for reform, and to ensure that technology is used to enhance equal opportunity – not undermine it.

Across all parts of American life, data is crucial for documenting persistent inequality and discrimination. For example, Census data are pivotally important for ensuring equitable political representation and guiding the budgeting process for important federal programs.

At the same time, discrimination and injustice are embedded in our society. They are reflected in the data that drive big data decisions— in credit histories, in housing patterns, in arrest records, and more.

Surveillance has long been misused against minorities: from antebellum slave patrols and FBI spying on civil rights activists, to more recent immigration enforcement against Latinos based on their names or skin tone and police profiling of Arab Americans, Sikhs, South Asians, and Muslims. And with greater access to big data and surveillance technology, our nation’s core civil rights protections are being undermined in ways unimaginable in even the most recent past.

Sophisticated data mining can now prejudge entire communities. Actuaries and data brokers are now able to lump individuals into categories using neighborhood data, consumer purchase history, financial decisions of neighbors, and any number of metrics that serve as proxies for race and class.

Lenders now micro-target their products in ways that exclude consumers categorized as “Ethnic Second-City Strugglers” and “Credit Crunched: City Families.” This micro-targeting is circumventing our nation’s fair lending laws by pre-determining whether low-income people and people of color ever even get the chance to fill out an application. And this micro-targeting can cut both ways by empowering lenders to market only the most predatory and high-risk financial products to these communities.

Low-income people, especially those on public assistance, are more vulnerable to having their privacy violated. Government-administered social programs collect massive amounts of data about applicants with notoriously uneven levels of security. In Utah, for example, a state worker accessed the benefits database and released its contents to media and law enforcement – all based on her personal suspicion that they might identify unauthorized immigrants. Just two years later, Utah’s database was hacked and the information of 750,000 people was released. As these benefits systems migrate online, the potential for more cybercrimes and identity theft becomes even greater.

With the right policies, big data could be helpful in the fight for equal protection under the law. Data can shed light on inequality and discrimination, and can chronicle our nation’s progress on narrowing disparities. But without reform, high-tech discrimination will continue and grow in law enforcement, lending, employment, immigration, education, and other areas – in ways seen and unseen.

The civil rights community has begun to voice its concerns and the White House and Congress should take notice.

Wade Henderson is president and CEO of The Leadership Conference on Civil and Human Rights. Rashad Robinson is the executive director of ColorOfChange. Their organizations are signatories of the recently released Civil Rights Principles for the Era of Big Data.

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