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.
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.
Now, algorithms are being used to help managers measure productivity and make important decisions in hiring, compensation, promotion, and training opportunities — all of which may be life-changing for employees. Firms are using this technology to identify and close pay gaps across gender, race, or other important demographic categories. HR professionals routinely use AI-based tools to screen resumes to save time, improve accuracy, and uncover hidden patterns in qualifications that are associated with better (or worse) future performance. AI-based models can even be used to suggest which employees might quit in the near future.
And yet, for all the promise of people analytics tools, they may also lead managers seriously astray.
Amazon had to throw away a resume screening tool built by its engineers because it was biased against women. Or consider LinkedIn, which is used all over the world by professionals to network and search for jobs and by HR professionals to recruit. The platform’s auto-complete feature for its search bar was found to be suggesting that female names such as “Stephanie” be replaced with male names like “Stephen.” Finally, on the recruiting side, a social media ad for Science, Technology, Engineering and Math (STEM) field opportunities that had been carefully designed to be gender neutral was shown disproportionately to men by an algorithm designed to maximize value for recruiters’ ad budgets, because women are generally more responsive to advertisements and thus ads shown to them are more expensive.
In each of these examples, a breakdown in the analytical process arose and produced an unintended — and at times severe — bias against a particular group. Yet, these breakdowns can and must be prevented. To realize the potential of AI-based people analytics, companies must understand the root causes of algorithmic bias and how they play out in common people analytics tools.
Data isn’t neutral. People analytics tools are generally built off an employer’s historical data on the recruiting, retention, promotion, and compensation of its employees. Such data will always reflect the decisions and attitudes of the past.
