2019-12-17-hrexaminer-article-by-john-sumser-3-takeaways-for-AI-in-HR-Tech-from-iCIMS-inFLUENCE-event-photo-img-cc0-via-unsplash-markus-spiske-TaKB-4F58ek-544x361px.jpg

“Machines Learn and develop Opinions. The Machine Recommends. Humans must make the decisions while the organization inherits the liability. Saying that ‘the computer made me do it’ won’t be a workable defense. Make sure your vendor discloses every aspect of their algorithms and test to see if the systems recommend your people.” - John Sumser

Three things all companies should be doing with their HR Tech
to prepare for their future with AI and Intelligent Tools.

 
I participated in a fantastic conversation at this year’s iNFLUENCE event. The panel, led by Google’s Tarquin Clark, was an assemblage of an excellent range of perspectives. Tarquin provided historical context and convivial moderation of the group.

Amy Loomis from IDC brought her in-depth expertise on the Future of Work to the party. iCIMS’ own Peter Hagelund voiced the company’s no-nonsense, risk-mitigating strategy. Alen Brcic from Novant Health provided the realistic insight of a successful AI implementer.

At each step of the preparation process, the iCIMS team underlined the importance of having an open conversation that did not mimic the company line. I rarely get the opportunity to participate in a genuinely open dialog. It’s a kind of respect for customers that shows how critical their success is to the iCIMS team.

In many cases, enterprise software companies in HR Tech work hard to keep divergent points of view away from the customer base. Nothing could be less helpful when it comes to navigating the emergence of intelligent tools and AI in HR. By salting the panel with independent experts, iCIMS guaranteed that their audience would get closer to the truth. They provided an excellent foundation for customers who want to build an ecosystem of intelligent tools.

We are in the very earliest stages of the introduction of AI and related tools to the worlds of HR and Recruiting. It’s a lot like the early days of the airplane or the automobile. There were about 80 companies investigating flight when the Wright Brothers first flew. It took a very long time for their triumph to morph into useful aviation. There were lots of opinions about what would, could, or should work.

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John Sumser is a Principal Analyst for HRExaminer.

In our world, there is an overabundance of claims and a shortage of real-world experience. As I said, it’s the earliest of days. The smart thing about the iCIMS approach is that it lets the market settle on whether or not the claims bear fruit. You might sum the philosophy as ‘we don’t want to be a leader in technology; we want to be a leader in customer value.’ iCIMS CTO, Al Smith, in his remarks earlier in the day, said as much.

Here are a few tidbits from my part in the conversation.

Three things all companies should be doing to prepare for their future with AI.

  • Training: Teach employees to argue with the machine. Machine intelligence produces probable insights. Like Vegas odds, they are not a guarantee. The best thing you can do is start by assuming that the recommendations are wrong.
    • Ethics: Build an ethics board that includes people outside HR and the company. Ask – What could go wrong? The model iCIMS set in this conversation, a desire for expansive open communication, is how the ethics team should evolve.
    • Legal: Never put an algorithm in play without a review by your company lawyer. You need to know where the intelligence is in your software. Anytime a machine gives decision making input, it should be vetted for bias, appropriateness, compliance with policy, and unintended consequences. Since the employer is liable for the results of the recommendation, it’s best to keep the lawyers involved.

Here’s a deeper dive into those three topics:

  • Machines Learn. That means that their performance will be variable. Work with the vendor to establish a reliable way to monitor the variations.
  • Machines have Opinions. Humans make decisions. Be prepared to trust your gut even though the device holds the data.
  • The Machine Recommends. The organization inherits the liability. Saying that ‘the computer made me do it’ won’t be a workable defense.
  • Make sure the vendor discloses every aspect that has an algorithm or model. For instance, automated interview scheduling tools will perpetuate the bias in the hiring system. Meanwhile, the underlying algorithm or the fact that machine learning is involved may not be apparent to you. Always get a disclosure describing every place where intelligence occurs to the process.
  • Test to see if the systems recommend your people. Finally, the only way you can be sure that the machine is doing what you want it to do is by calibrating with test data. One good way to do this in HR and Recruiting is to make sure that the system always recommends your current team first. Get their data into the system and use it as the primary benchmark.

The panel at iCIMS iNFLUENCE event established a model that ought to be followed by software companies in general. By treating their customers as intelligent consumers, iCIMS demonstrated their commitment to customer success on customer terms. It was a privilege to be a part of it.



 
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