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  • Algorithms break! When considering the use of a tool that learns from experience, consider the downsides. If there is risk involved in the decisions (say, wreaking havoc in the lives of others, multiplying existing bias or increasing the very attrition you are trying to reduce), you’ll need to spend real money supervising the errant tool. This thoughtful piece lays out the risks and economic consequences.  To err is algorithm: Algorithmic fallibility and economic organisation
  • The Current State of Machine Intelligence 3.0. “Machine intelligence business models are going to be different from licensed and subscription software, but we don’t know how. Unlike traditional software, we still lack frameworks for management to decide where to deploy machine intelligence.”
  • If You Want to Be Creative, Don’t Be Data Driven. Data is not reality. All data misses something.

HR’s View

  • Pater Capelli’s delightful rant in the Harvard Business Review, There’s No Such Thing as Big Data in HR. Capelli makes the compelling case that nothing matters if you don’t have real quality in your data. Most internal HR implementations are better understood as spreadsheets.



  • 10 Common NLP Terms Explained for the Text Analysis Novice. Natural Language processing is the emerging tool for making sense out of large chunks of unstructured data. Your Introduction Starts here.
  • The Strange Loop in Deep Learning. I wish I could say that I understood everything in this compact piece. What I got is the idea that in very advanced AI/ML, it’s possible to move backwards and forwards in the learning. You can go back to the way it was five iterations ago. This functionality is not a part of the AI/ML in HRTech. This is one of those that I have to scan multiple times to wring all of its value out.

Quote of the Week

“Algorithms need to be applied much more carefully in domains where the penalties from errors are high, such as health or the criminal justice system, and when dealing with groups who are more vulnerable to algorithmic errors. Only highly accurate algorithms are suitable for these risky decisions, unless they are complemented with expensive human supervisors who can find and fix errors. This will create natural limits to algorithmic decision-making: how many people can you hire to check an expanded number of decisions? Human attention remains a bottleneck to more decisions.”
– To err is algorithm: Algorithmic fallibility and economic organisation


Curate means a variety of things: from the work of vicar entrusted with the care of souls to that of an exhibit designer responsible for clarity and meaning. At the core, it seems to mean something about the importance of empathy in organization. HRIntelligencer is an update on the comings and goings in the Human Resource experiment with Artificial Intelligence, Digital Employees, Algorithms, Machine Learning, Big Data and all of that stuff. We present 8 to 10 links with some explanation. The goal is to give you a way to surf the rapidly evolving field without drowning in information.

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