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Written By ESR News Blog Editor Thomas Ahearn

In May 2016, the White House released ‘Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights’ that revealed “Big Data” – defined as information so high in volume, velocity, and variety that it requires new forms of processing for decision making – can uncover or even reduce employment discrimination but also risk enabling and automating potentially discriminatory hiring practices.

The report – the third in a series by the Obama Administration’s Big Data Working Group – challenges the assumption that Big Data is objective:  It is often assumed that big data techniques are unbiased because of the scale of the data and because the techniques are implemented through algorithmic systems. However, it is a mistake to assume they are objective simply because they are data-driven.

Instead, a White House blog about the report entitled ‘Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights’ states that while the report “illustrates how big data techniques can be used to detect bias and prevent discrimination. It also demonstrates the risks involved, particularly how technologies can deliberately or inadvertently perpetuate, exacerbate, or mask discrimination.”

With regard to employment discrimination, the report found that since traditional hiring practices may unnecessarily filter out applicants with skills match the job opening, companies looking to hire “turned to new ways of rating applicants, using Big Data analytical tools to automatically sort and identify the preferred candidates to move forward in a hiring process” through using algorithms and large data sets.

However, the report also found that Big Data technology was still controlled by humans and thus potentially discriminatory: Yet even as recruiting and hiring managers look to make greater use of algorithmic systems and automation, the inclination remains for individuals to hire someone similar to themselves, an unconscious phenomenon often referred to as “like me” bias, which can impede diversity.

As a result, this “like me” phenomenon in humans can make well intentioned Big Data technology fail: Algorithmic systems can be designed to help prevent this bias and increase diversity in the hiring process. Yet despite these goals, because they are built by humans and rely on imperfect data, these algorithmic systems may also be based on flawed judgments and assumptions that perpetuate bias as well.

But companies can also use Big Data to find potential employees who might be overlooked based on traditional educational or experience requirements. Big Data analytics systems that “allow companies to objectively consider experiences and skill sets” have proven successful. By looking at successful skills of previous employees, a Big Data system can “pattern match” to recognize skills new hires should possess.

Big Data systems can help combat bias in traditional hiring practices that can lead to discrimination by making “fairness, ethics, and opportunity” a core part of the original design: Beyond hiring decisions, properly deployed, advanced algorithmic systems present the possibility of tackling age-old employment discrimination challenges, such as the wage gap or occupational segregation.

Companies have begun to filter their applicant pools for job openings using various human resources analytics platforms. It is critical to the fairness of American workplaces that all companies continue to promote fairness and ethical approaches to the use of data tools and ensure against the perpetuation of biases that could disfavor certain groups.

The ‘Big Data and Employment’ section of the report concludes: Businesses also stand to benefit, because those that do not look beyond historical hiring patterns (even as mediated by an algorithm) will miss great candidates for important jobs. The complete White House report on Big Data is available at

As reported earlier on the ESR News Blog, a report released by the Federal Trade Commission (FTC) in January 2016 called ‘Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues’ that also provided recommendations to U.S. businesses on how to properly use Big Data analytics while also highlighting both the benefits and risks of Big Data for consumers in the United States.

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