We surveyed over five-hundred HR executives to find out what they **really** think about AI and machines replacing people.
Watch this video Q&A with John Sumser on AI and Intelligent Tools in the HR and Recruiting space during the coronavirus pandemic.
“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
“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
“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
“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
“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
“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
“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
“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
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