Can we explain AI with experiential? I say yes.
What we talk about when we talk about deciding: Notes from DAAG 2019
I’ll be at VentureBeat’s Transform AI conference July 10-11 in San Francisco. Let me know if you’re attending; would be great to meet. -Tracy It’s not always easy staying on the AI bandwagon. Claims of algorithmic bias abound and (mis)applications threaten people’s trust. Not everyone wants their face recognized or their driver’s license scanned. Developers […]
Our #1 fear: Withering under scrutiny.
Since my work is about humans+AI deciding together, I attended DAAG 2019 in beautiful downtown Denver, exploring the “intersection of decision analysis and data science to take decision-making to the next level.” The intent was for decision analysts to better understand data science and “support data-centric decision-making” while data scientists could better “guide the use […]
Don’t build a data department store.
Jerry Seinfeld was wrong when he claimed public speaking is our #1 fear. I’m pretty sure we’re more afraid of having our decisions scrutinized. Adding to the fun, now algorithmic decisions are under pressure too. It is rather painful to have decisions second-guessed before the numbers come in, and even worse if things go pear-shaped. […]
Machines Gone Wild! + Can Microlearning improve Data Science training?
To paraphrase Raymond Chandler, too many projects deliver department store data: The most of everything but the best of nothing. Enterprise AI and analytics developers must avoid the mistake of underserving people by overengineering solutions. Designers and decision makers need straightforward tools to make them better, to save time and facilitate their best work. They […]
1. Machines Gone Wild → Digital trust gap Last year I spoke with the CEO of a smallish healthcare firm. He had not embraced sophisticated analytics or machine-made decision making, with no comfort level for ‘what information he could believe’. He did, however, trust the CFO’s recommendations. Evidently, these sentiments are widely shared. — […]