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.
Hiring Algorithms Are Not Neutral
Don’t let the software screen out good candidates.
December 09, 2016
New!
HBR Learning
Digital Intelligence Course
Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Excel in a world that's being continually transformed by technology.
Learn More & See All Courses
For HBR Subscribers
AI, Algorithms, and Bias
Algorithms can improve our predictions and decisions, but they can also perpetuate our blind spots and biases. Here’s what you need to know about the problem—and how organizations can address it.
Show Reading List
New!
HBR Learning
Digital Intelligence Course
Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Excel in a world that's being continually transformed by technology.