A few weeks ago I published the introductory piece to this called “The Robots are Coming! (Why HR Should care about machine learning). In the next few posts we are going leap into more of what you need to know to embrace this change and make it a part of your future. If you are a nerd like me, you occasionally ponder about how to survive the future workforce. I’m not sure if it is going to look more like Terminator, iRobot, or Wall-E. No matter what, we should pursue whatever we can do to make the future a lot less Terminator-y.
Since the robot references related to automation can get a bit old, I’m going to change it up a bit. Recently the Jurassic World 2: Fallen Kingdom Poster was just released and the movie will be out in a year. As a point of reference, I’ll refer to the franchise as to point out one of the first key risks to be aware of as an HR professional dabbling into machine learning.
You may say “But I don’t understand advanced statistical programs, or want to learn how to do machine learning, or programming or any of that. Besides, HR doesn’t really have a seat at the table when it comes to topics like machine learning.”
Fair enough. But you have a choice. You can do be the lawyer in the original Jurassic Park and hide in the bathroom stall until the dinosaur eats you. Or you can be like Owen in the Jurassic World, becoming the “Alpha” leader in your organization and maintain eye contact and live on to another sequel.
Doesn’t seem like much of a choice, does it?
Here is a big lesson (and risk) that you need to know about machine learning:
The robots can learn from data it’s not supposed to have.
Imagine this scenario. Some vendor comes to present to your team and describes their giant, fancy “deep-learning” artificial intelligence system to predict which employees have the underlying characteristics to be promoted. The claim is that because it’s a model, not managers making judgements, that it is more accurate and not prone to human bias. You can spot great talent using the data, not managers who might be holding them back.
“That’s a great thing,” says your tech-inspired/risk averse leader. “Bias is poisonous to the workforce. We should let the data speak for itself, and find that next generation of talent.”
The rep smells opportunity. “We can load in your employee demographics, but leave out race, gender, and age since we wouldn’t want the model to be influenced by those variables.”
Time for your smart question.
“How do you know that all this data you’re collecting and training the machine on isn’t biased? If there is some underlying bias in the behavior you are tracking, how do you know that it’s not picking that up?”
The answer you get will probably be like in Jurassic Park, when the protagonists asked the DNA splicing scientist how they kept the dinosaurs from breeding. The response was, very logically and confidently, that they only release female dinosaurs so that they can’t reproduce.
Just as Jeff Goldblum’s character famously stated, “Life Finds a Way.” In the data science world, the data can find a way.
Let’s talk about who normal employee data can accidently be biased by race or age. The underlying premise is that other data points can often be very good proxies for those demographic factors. Those other variables don’t need to be causal – if there is a relationship between a benign data point and something like race, the model can accidently learn to be racist if that correlated variable influences a rule.
Unfortunately, most sensitive demographics tend to have normal variables that correlate.
Race might be correlated with zip code, region of the country, or even someone’s social network if that is part of an analysis. Race may also unintentionally pop up in something like college choice, if you had a large number of graduates from the University of Alabama and other graduates from Alabama A&M University, if there were differences in their treatment in the workforce the computer might not realize that one of those is a HCBU school with a high-correlation in racial differences between those schools.
Age is easily derived through past work experience, and it’s not hard to see how something as simple as a personal email address might indicate age (how many millennials have an @AOL.com email address?)
Factors like gender may have correlations to full-time or part-time status or with breaks in service or any number of other differences that may trend in HR data.
Remember, too, that machine learning starts with the machine learning from a set of scenarios and outcomes. If the machine is learning about “who is a good employee” from your employment rating system, and you had managers who were biased against a certain gender, race, or age of employee – then those outcomes would be viewed by the machine as being correct, even if they were biased. And if you say let’s use something more “objective” like historical career track (who progressed the most quickly), you might still be perpetuating any underlying biases that impact that career track. In effect, the computer may just codify the things you were afraid your managers were doing all along.
Before you say “That’s an outrageous hypothetical you came up with” – I’d challenge everyone interested in this area to watch this TED talk.
Going back to Jurassic Park, where late in the movie, the team accidentally stumbles onto a nest dinosaur eggs. Despite the earlier re-assurances of no male dinosaurs being released, there is the sudden realization that by using frog DNA to fill in the gaps in the dinosaur DNA, that they gave the dinosaurs the ability to reproduce asexually, and that “Life finds a way”. That makes for a good movie twist, but it is a bad outcome if it is an oversight on your part in your predictive learning system.
I give all these examples not to suggest that every machine learning system will have these problems, or that you can’t correct these problems. The architects of machine learning products, who are brilliant enough to build these programs and models, are also smart enough to control for these biases if they are asked to do so. But if the super-users and consumers of these products – yes, that’s you – are not even aware of the potential unintended consequences, then how likely is it to be requested?
You won’t be eaten by a bad algorithm. But being sued isn’t out of the question.
Maybe robots, dinosaurs, and HR work are all completely different things, but if the movies have taught me anything, it’s that almost anything brilliant that we build can get away from us if we aren’t careful.
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This reminds me of a Dilbert cartoon: http://dilbert.com/strip/2008-07-16 where Catbert asks him to make a choice.
Now lets say a automated car is going to crash, and it has to choose between a child and a mother and a solo child. The car chooses to hit one child instead of hitting two. The mother and child live and now the mom dresses her child in a jacket with numerous faces on it. Later on another automated car is again going to crash and now the choice is the grown-up child wearing the ‘faces’ jacket mom made him wear and another child. The automated car now has to make the same ethical choice of hitting what it thinks are fewer people.
Love the cartoon!
The concept of using the rules of the game to win the game is certainly nothing new, and as more automation arrives people will learn to work with it. A Google search was simply black magic to most people not long ago. Once the number of links became well-known as a driver, link farms became real… and Google adapted the algorithm. Now there is an entire industry around SEO to maximize performance within the rules. While I hope your example never happens, it certainly highlights the issue.
Jackets with faces on them to improve your life’s worth in the eyes of a computer. Now I’m thinking about self-driving cars and how the road runner used to paint a tunnel on the side of a mountain… it’s like there will be a whole new set of crimes and mishaps that will exist that we haven’t even started yet.
Great article, Bryan! This is directly inline with a conversation that I had recently with a friend over at the National Weather Service in Romeoville. The NWS has been trying to predict the weather using computers since 1950. While they are generally pretty accurate, they are a long way from being perfect… and they have the ability to easily measure the accuracy of their predictions. They know EXACTLY the outcome of those predictions… temperature, wind speed and direction, precipitation amounts, cloud cover, etc. Predicting human behavior will never have that level of ability to observe how accurate the predictive models are.
Here’s what I learned from my conversation… the NWS routinely use for near term forecasting 1.9 square mile grids. So assuming the U.S. is about 3.797 million square miles, that would be almost 2 million grid points. For each of these grid points, the most basic variables would be temperature, dew point, pressure, wind speed and wind direction. Using only these variables would result in a very basic forecast. The reality is that these atmospheric variables and many others such as radar and satellite data are assessed over time and space (vertical and horizontal) and combined with other non atmospheric variables such as soil moisture, snow cover, water temperatures and so on. These variables are plugged into equations to output other variables that are then used in even more equations. Other assumptions are made as well. And the quality of the forecast is directly related to the quality of the data going in. In any given model, there are hundreds of variables if not more.
Given all of this, assuming that machine learning in HR will accurately predict employee behavior, motivations, and efficacy in our life time with any degree of accuracy seems like a stretch. If the NWS has been working with their computer models for close to 70 years leveraging hundreds of data points AND they are able to EXACTLY measure the accuracy of their predictions, it seems like our managers will continue to be relied upon to make informed organizational decisions.
You raise a good point about being able to review the accuracy of prediction. Models have to learn about what is “right” which can be difficult with humans.
I do, however, that we can use data to predict many aspects of HR decisions. I may not be able to predict who will quit and why, but I can probably predict how many people will quit and in certain departments or locations. Or, selecting a handful of candidates who are more likely to make great hires, not declaring from data alone which candidate will be a superstar. I’m not trying to predict that it will be exactly 39 degrees and will rain .02 inches… I’m trying to predict whether we should wear a sweater and have an umbrella nearby.
Absolutely, Paul! While we might not be able to perfectly predict with exactness, that doesn’t mean that we should not leverage predictive models where we can to better understand and engage our employees. There are a whole variety of technologies that allow us to capture new data points that provide greater insights into employee sentiments and behavior. But as Bryan points out in his article, it’s important that we understand what we’re asking of the data and what biases may be lurking under the surface. The next five years are going to be exciting!