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Hiring Algorithms Are Not Neutral

By Gideon Mann & Cathy O'Neil

SOURCE: Bloomberg


Originally posted on Harvard Business Review.

By Gideon Mann & Cathy O'Neil
Gideon Mann is head of data science at Bloomberg LP.

More and more, human resources managers rely on data-driven algorithms to help with hiring decisions and to navigate a vast pool of potential job candidates. These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. But there are drawbacks to this level of efficiency. Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them.

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KEYWORDS: Diversity & Human Resources, demographics, algorithms, data science, machine learning, Gender, Race, diversity, Author, math, hiring, Careers, jobs, Education

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