Does Your AI Need A Background Check and a Reference?
In June, I start seriously looking forward to the annual HR Technology Conference in October. There is a rhythm to the year that seems to begin and end with the encampment in Las Vegas, considered the industry’s town hall. We get together and show each other what we’ve learned and created over the past 12 months.
For most of the 2010s, the story was the increasing importance of data in the human resource profession. We have all been learning how to effectively incorporate data into our decision-making. At first, the hype focused on big data. Then it was people analytics. This year, don’t be surprised if everyone at the conference is telling you about their artificial intelligence.
It’s essential to notice how the story stays the same even when the language is changing. Big data, people analytics and artificial intelligence are really just pieces of one thing. More data plus more processing yields better understanding. How you categorize it matters much less than how you evaluate it and how you use it.
Currently, the story is about all the ways machine intelligence is creeping into every decision we make. Like ivy on stone walls, the stuff is everywhere. Because they have tons of data, enterprise providers are embedding intelligence in every aspect of their suites.
It’s like having a flock of new workers offering insights, recommendations, forecasts and predictions about every aspect of our processes and outputs. We are introducing these fast-learning but young and inexperienced workers into our organizations. The good news is that they learn quickly—the bad news is that they are more emphatic about their output than the work actually deserves.
Meanwhile, industry-leading HR departments are quickly deploying teams of data scientists to make sense of all the new information and recommendations. These early stages of HR as a science involve investigating correlations between every imaginable piece of data for clues to workforce productivity, predictions of employee behavior, selection suggestions and the ways HR drives business results.
In short, algorithms, models, correlations, predictions, recommendations and forecasts are coming towards us in a blaze of potential insight. The key question is whether or not you can trust what you see. Do a ton of machine processing and math actually improve on historical efforts? Does the underlying model adequately describe the reality it predicts?
The answer, unsurprisingly, is that nobody can tell at first. Although a machine’s output has a certain ring of authority, you’re really dealing with a new worker. As with all junior employees, trust should be earned over time.