Big Data’s Disparate Impact

Big Data’s Disparate Impact

2016 | Solon Barocas* & Andrew D. Selbst**
The essay "Big Data's Disparate Impact" by Solon Barocas and Andrew Selbst examines the potential for algorithmic techniques, particularly data mining, to perpetuate and exacerbate societal biases and inequalities. While advocates argue that these techniques eliminate human biases, the authors highlight that algorithms are only as good as the data they process, which can be imperfect and reflect existing societal prejudices. The essay focuses on American antidiscrimination law, specifically Title VII of the Civil Rights Act, to explore how data mining can lead to disparate impact, where the effects of a practice disproportionately harm protected groups without intentional discrimination. The authors discuss the challenges of identifying and addressing unintentional discrimination, the difficulties in reforming the law to accommodate these issues, and the tension between anticlassification and antissubordination theories. They argue that the current legal framework, which emphasizes procedural fairness, may not adequately address the complex and often subtle ways in which data mining can disadvantage historically disadvantaged groups. The essay concludes by emphasizing the need for a broader reexamination of the concepts of "discrimination" and "fairness" to effectively tackle the disparate impact of big data.The essay "Big Data's Disparate Impact" by Solon Barocas and Andrew Selbst examines the potential for algorithmic techniques, particularly data mining, to perpetuate and exacerbate societal biases and inequalities. While advocates argue that these techniques eliminate human biases, the authors highlight that algorithms are only as good as the data they process, which can be imperfect and reflect existing societal prejudices. The essay focuses on American antidiscrimination law, specifically Title VII of the Civil Rights Act, to explore how data mining can lead to disparate impact, where the effects of a practice disproportionately harm protected groups without intentional discrimination. The authors discuss the challenges of identifying and addressing unintentional discrimination, the difficulties in reforming the law to accommodate these issues, and the tension between anticlassification and antissubordination theories. They argue that the current legal framework, which emphasizes procedural fairness, may not adequately address the complex and often subtle ways in which data mining can disadvantage historically disadvantaged groups. The essay concludes by emphasizing the need for a broader reexamination of the concepts of "discrimination" and "fairness" to effectively tackle the disparate impact of big data.
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