“Jim Collins originally used the Flywheel as a metaphor in his book, Good To Great. Picture a huge, heavy flywheel mounted horizontally on an axle. The flywheel is two feet thick and weighs five-thousand pounds. Now imagine your task is to get the flywheel rotating on the axle as fast and for as long as possible.” - John Sumser
“What we’re talking about here is the emergence of a new set of analytics-driven data-based elements of the HR department. It’s part of a new era in HR management that will evolve over the next two to five years. This is really interesting stuff.” - John Sumser
Behind the uncoded bias in AI hiring are machines participating in and perhaps even dictating hiring decisions.
“Every time we generalize about people or information or ideas, bits of detail get lost. To define something is to limit it. The very act of putting something complex and nuanced into a more general description, diminishes what is possible to convey about the thing itself. Representations are never complete.” - Heather Bussing
“While it is true that a machine can do a better job at relentlessly sticking to a narrow script, it cannot see or understand things that are not in the data. Unlike people and their unconscious biases, machines can only change their approach with new measurement and new coding. In other words, while machines may be able to address small components of unconscious bias, they cannot address all (or even most) of it.” - John Sumser
“Allowing machines to participate in and perhaps even dictate decisions in Human Resources raises a host of ethical considerations. After all, these systems control various forms of opportunity for the workforce. More precisely, they involve people’s livelihood, hopes and dreams.” - John Sumser
“Bias is always an issue with AI because the machine learning systems only know what they are taught. And what machine learning systems ‘‘learn’ is sometimes surprising or just wrong.” - Heather Bussing
“Bias is always an issue with AI because the machine learning systems only know what they are taught. And what machine learning systems ‘learn’ is sometimes surprising or just wrong.” - Heather Bussing
“Every HR professional should be prepared to face data that contradicts their beliefs sooner or later.” – Stacey Harris
“For those looking to figure out solutions to the problems facing their respective organizations or careers, learn to develop, refine, and trust your gut. ” - Victorio Milian
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