Onboarding the algorithms
During the Ideas and Innovators session at the HR Technology Conference last month, my pal Michael Krupa gave an outstanding talk about automation and advanced ‘learning’ kinds of technology, and some of the implications for HR and organizational leaders who are choosing to incorporate these technologies into their people and talent management programs.
In the talk, (and once I get the video of this, I will be sure to update the post with the link), Michael used a great expression to illustrate the deliberate and measured approach HR should take to adoption of these ‘smart’ tools. He called it ‘Onboarding the algorithms’; a way of comparing the introduction and deployment of these technologies to the structured and immersive process that most organizations follow when onboarding new employees. The larger point – HR and business leaders need to carefully evaluate, understand, assess, and introduce algorithms and other advanced, intelligent, (and often predictive), tools carefully, and insert them into the HR and talent processes intelligently and intentionally – just like we do when hiring and welcoming new employees.
I think Michael’s analogy was a great one as it serves as a kind a warning to HR and business leaders eager to adopt these kinds of advanced and predictive tools for functions like evaluating a slate of job candidates, making decisions about which employees should be considered ‘high potential’, and thus granted more development and growth opportunities, scheduling the ‘optimal’ mix of employees for a given day or shift, or to provide intelligence and decision support to managers making decisions about the allocation of salary and bonus pools.
I have no doubt that organizations and HR leaders will seek to adopt these kinds of tools more and more in 2017, but at the same time I think it also we be important, to use Michael’s phrase, to ‘onboard’ these tools and algorithms effectively, in order to ensure we not only utilize the tools to their potential, but we also understand how these tools are actually designed and how they are performing. Kind of like how we know the background of every new employee that comes onboard the organization and how we like to keep track of their assimilation and performance – usually in a pretty structured 30-60-90-180 day kind of manner.
This is a pretty important and complex idea for sure. One that can’t be completely explained in one blog post. But I think one good place for HR and business leaders to start is to have really open and honest conversations with their HR technology providers of these smart tools and algorithms to gain a better understanding of how they are designed, how they work, (or are meant to work), and how transparent are the machine’s thought processes they end up in a decision, (or at least a recommendation, i.e. ‘Hire this person, and not that person.’).
A great starting point for HR leaders who need to know what questions to ask of your HR tech providers is the Principles for Accountable Algorithms statement from the Fairness, Accountability, and Transparency in Machine Learning organization. They break down the five key guiding principles for algorithm design, (responsibility, explainability, accuracy, audibility, and fairness), that HR leaders can look for, (and seek answers about), from their technology providers. Of course there is plenty more for HR leaders to consider when evaluating and deploying these tools, but the FAT/ML document provides a good starting point. You can learn more about that organization and what they do here.
Just like every good HR pro knows that we just can’t toss new employees blindly into the fire and expect that they will produce desired outcomes, (and be happy), we can’t expect that we can simply insert new technologies, no matter how ‘intelligent’, and think we will get optimal outcomes. If these tools are meant to become a fundamental element of you and your leader’s talent and people management playbook, then you need to understand them as well as you need to understand all the members of your team.
And you need to be able to tell when the algorithm is wrong too.
Have a great week!
via Steve Boese’s HR Technology http://ift.tt/MS9XNl
November 28, 2016 at 12:21AM