WHAT EVERY MANAGER SHOULD KNOW ABOUT HUMAN-CENTERED AI: A Manager’s Introduction to Human-Centered Artificial Intelligence
As AI advances, there is a need for better frameworks to understand how to create value from changing human-AI relationships. This article offers a practical guide for managers and executives looking to leverage human-centered approaches to AI.
“I think the problem was that our systems designed to recognise, and correct human error failed us.”1 This was the explanation given in 1999 by Carl Pilcher, science director for solar system exploration at NASA’s Jet Propulsion Laboratory, when a failed measurement conversion led to the 125 million Mars Climate Orbiter slamming into the Martian surface. This problem was succeeded 14 years later when a calculation error in the design of a new Spanish submarine2 led to a fatal flaw, such that the submarine could submerge but could not resurface – an error causing a 2.2 billion dollar investment to be delayed by years. Each of these errors was a simple issue of calculation and translation, ultimately a matter of mistaken decimal points. So, what if a system could automatically catch all such errors?
Successful AI projects are less about the tech than the institutions and practices to which they are connected.
Naturally, the suspicion has already been investigated, with novel artificial intelligence (AI) systems being deployed across sectors to reduce rates of human error. Across such projects a common theme has emerge – AI systems may be good at correcting for problems identified, but they are not very good at independently identifying what counts as a problem that needs to be corrected outside
