Q&A on AI and Intelligent Tools with John Sumser

Watch this video Q&A with John Sumser on AI and Intelligent Tools in the HR and Recruiting space during the coronavirus pandemic.

User Interface Design Ethics in AI – Part II

“One area Manhattan-based Greenhouse focused on is predicting the Recruiting Department’s results. Hiring and finance managers always want to know when the new person will be hired and whether they will show up.” - John Sumser

User Interface Design Ethics in AI – Part I

“The essence of traditional interfaces is a deep emphasis on clarity (or intuitiveness). One look at the interface tells you what to do. That doesn’t work with likelihoods. Intelligent output like you’d find in machine learning requires the user to think before deciding.” - John Sumser

HR Tech: AI and Intelligent Software Implementation – Part II

“From data quality to regulatory compliance, there are 28 key parameters to evaluate when considering a purchase of intelligent tools for HR. We’ve also provided 40 key questions to ask to evaluate an AI or Intelligent Software vendor solution.” - John Sumser

HR Tech: AI and Intelligent Software Implementation – Part I

“The question is when, not whether, organizations will use intelligent tools. The future is inevitable. The sooner you get started, the easier it will be to keep up. Conversely, the longer you wait, the more competitive advantage you will lose.” - John Sumser

Can we recognize baked-in AI in HR Tech in order to manage it properly?

“AI is quickly becoming a table-stakes commodity. Since it will often be unlabeled, the question will be, ‘How do we recognize it?’ This is not the discovery of a rarity, it’s remembering to be effective in the flow of recommendations, suggestions, forecasts, probabilities, and interpretations that fill our every transaction. Recognizing AI means trying to remember, in the onslaught of machine opinion, that by accepting the machine’s opinion you are making a decision.” - John Sumser

Bias in AI: Are People the Problem or the Solution?

On December 17, 2020, in AI, AI Ethics, Artificial Intelligence, HR Tech, HR Technology, John Sumser, Machine Learning, by John Sumser
“All tools contain embedded biases. Bias can be introduced long before the data is examined and at other parts of the process. Meanwhile, one group says people are the problem; the other sees them as the solution.” - John Sumser

Modern HR Data Types and Attributes (from text to machine generated and monitored data)

“Data has a funny property. It wants to make more data. There’s a saying, ‘data makes its own gravy.’ Using data creates data about usage. Interestingly, the metadata created by data is often more useful than the data itself.” - John Sumser

How to Not Screw Up Predictions (Exercise: Consider and Decide)

“Predictions don’t give you answers. They give you more questions. And it’s essential to explore those questions before you make decisions based on predictions, especially when people and their careers are involved.” - Heather Bussing

How to Not Screw Up Predictions (when you’re working with AI and Intelligent Tools)

“One of the best things about predictions is that they tell you how often they will be wrong. If there is only a 10% chance of rain, you probably won’t take an umbrella. But one in ten times, you will still get wet. You will be surprised since there was a 90% chance of sun. But the prediction was correct.” - Heather Bussing