1974
Scott Morton started MIT's Center for Information Systems Research in 1974, before chief information officers existed in large organizations.
“he started the Center for IS research in 1974. This is before we had CIOs and large organizations were struggling with managing tech and succeeding in tech.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:03:05
2015
Around 2015, leaders working with CISR began asking about data creepiness, which led CISR to expand its data governance capability to cover values and ethics.
“back in about 2015, we started getting questions from the leaders with whom I work very closely about data creepiness.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:09:42
Undated
MIT's Center for Information Systems Research (CISR) recently celebrated its 50th anniversary, and its founder Scott Morton gave a talk.
“CISR just celebrated its 50th anniversary at MIT. In fact, we had the founder just give a talk, Scott Morton.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:02:13
Undated
CISR was established to help leaders succeed with technology, and Wixom says it is a nonprofit.
“CISR was established to help leaders succeed. It's actually a nonprofit, and we still do that today.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:03:05
Undated
CISR identified 52 AI projects in production when it began studying how to measure AI success.
“So at the time, we identified fifty two projects that had been put in production.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:18:52
Undated
CISR found that making machine learning projects deployable required a sixth capability, AI explanation, beyond the five data monetization capabilities.
“it required a six capability beyond my data monetization, five core capabilities, and it was AI explanation.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:20:03
Undated
CISR defines AI explanation as covering bias remediation, decision tracing, value formulation and boundary setting.
“AI explanation we define as a capability where you can do bias remediation, decision tracing, value formulation, and boundary setting.”
Barb Wixom · Monetizing Data With AI: MIT CISR's Barb Wixom, 0:20:33