People analytics, the application of scientific and statistical methods to behavioral data, traces its origins to Frederick Winslow Taylor’s classic The Principles of Scientific Management in 1911, which sought to apply engineering methods to the management of people. But it wasn’t until a century later — after advances in computer power, statistical methods, and especially artificial intelligence (AI) — that the field truly exploded in power, depth, and widespread application, especially, but not only, in Human Resources (HR) management. By automating the collection and analysis of large datasets, AI and other analytics tools offer the promise of improving every phase of the HR pipeline, from recruitment and compensation to promotion, training, and evaluation.
Using People Analytics to Build an Equitable Workplace
Automation is coming to HR. By automating the collection and analysis of large datasets, AI and other analytics tools offer the promise of improving every phase of the HR pipeline, from recruitment and compensation to promotion, training, and evaluation. These systems, however, can reflect historical biases and discriminate on the basis of race, gender, and class. Managers should consider that 1) models are likely to perform best with regard to individuals in majority demographic groups but worse with less well represented groups; 2) there is no such thing as a truly “race-blind” or “gender-blind” model, and omitting race or gender explicitly from a model can even make things worse; and 3) if demographic categories aren’t evenly distributed in your organization (and in most they aren’t), even carefully built models will not lead to equal outcomes across groups.
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