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HRIntelligencer v1.31

On December 27, 2017, in HR Intelligencer, HRExaminer, by John Sumser

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It’s easy to want to hide from the overwhelming blast of techno-babble and shiny new objects. You can’t, really. The idea behind the HRIntelligencer is to filter that flow so that you can stay aware and still get things done.

This week, some sobering looks at basic ideas. Pick a couple and let them surround you. It’s where we are at the end of 2017.

Big Picture
HR’s View
  • What Can Machine Learning Do? Workforce Implications. There is no widely accepted definition of the tasks that Machine Learning (ML) does well. That means that there are no simple answers to the question of which job or jobs will be automated. Sometimes, as in recruiting, the ethical risks have to be assumed by a human in spite of the machine’s best work. 
  • Consumers are Workers. French economist Antonio Casilli says that consumers are being underpaid for their work as consumers and that is the key to post automation employment.
Execution
  • Net Promoter Score Considered Harmful (and What UX Professionals Can Do About It). Most organizations are exposed to the notion that a Net Promoter Score is an interesting way to measure interactions. HR Departments can be seen using an NPS to measure user satisfaction, among other things. The NPS has been debunked many times. Here’s how to think about it. Read this as a way of thinking about the analytics you might be considering.
Tutorial

 

Quote of the Week

Any discussion of what ML can and cannot do, and how this might affect the economy, should first recognize two broad, underlying considerations.

We remain very far from artificial general intelligence;
Machines cannot do the full range of tasks that humans can do 

In other cases, machines will augment human capabilities and make possible entirely new products, services, and processes. Therefore, the net effect on the demand for labor, even within jobs that are partially automated, can be either negative or positive. Although broader economic effects can be complex, labor demand is more likely to fall for tasks that are close substitutes for capabilities of ML, whereas it is more likely to increase for tasks that are complements for these systems. Each time an ML system crosses the threshold where it becomes more cost-effective than humans on a task, profit-maximizing entrepreneurs and managers will increasingly seek to substitute machines for people. This can have effects throughout the economy, boosting productivity, lowering prices, shifting labor demand, and restructuring industries.”

From What Can Machine Learning Do? Workforce Implications

 

 

About

 
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 means 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 a few critical links with some explanation. The goal is to give you a way to surf the rapidly evolving field without drowning in information. We offer a timeless curation of the intersection of HR and the machines that serve it. We curate the emergence of Machine Led Decision Making in HR.
 

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