How to blame a robot for mistakes: Do your fingerpointing properly.
How Human-in-the-Loop AI is Like House Hunters
People and artificial intelligence work together, sometimes quite nicely, on insurance underwriting, medical diagnosis, customer experience. (Plus autonomous vehicles, until some dude crashes his Tesla.) This is the Human+AI hybrid we often talk about at Museum of AI. Humans have varying levels of awareness, input, or control over an AI system. Evidence suggests these factors […]
Deciding while distancing: From data viz to the hard decisions.
House Hunters International is great guilty-pleasure viewing, especially while nursing a cold or avoiding the plague. (Pro: Insider views of interesting cities. Con: Reminders of the unique pain of choosing a place to live.) It’s easy to add city center, natural light, and extra bedrooms to your wish list, but painful to accept the inevitable […]
Will military ethics principles make AI GRRET again?
This is an anxious time for anyone in policy / decision analysis. Machine learning, analytics, statistical models, and decision intelligence are helping leaders grapple with the coronavirus pandemic, but weighing alternatives and connecting actions → outcomes requires so many things. Once the modeling is complete and the data visualizations are shared, then what? Newly anointed […]
Struggling to explain AI? Try this before|after strategy.
U.S. Defense Secretary Mark Esper has announced the military’s five ethical principles for AI use. The devil will definitely be in the details because the guidelines are mostly a statement of values. But I already have concerns. Allow me to explain. Can ethical guidelines make AI GRRET again? I’ve acronymized the five principles as GRRET: “Governable. […]
Can we explain AI with experiential? I say yes.
To cut through AI complexity, focus on decisions. Never has it been more important to effectively explain complex concepts. Technology is influencing most decision processes, not always transparently so. On the bumpy road toward explainable AI (XAI), we find great communication options, from printed materials to state-of-the-art experiences. But where do you start, or know […]
How fun & games can make us better at deciding.
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 […]
What we talk about when we talk about deciding: Notes from DAAG 2019
What is a fun experience really about? I’ve gained new perspective from Fun, Taste, & Games: An Aesthetics of the Idle, Unproductive, and Otherwise Playful (MIT Press). I have first-hand knowledge that the authors, John Sharp (Parsons/New School) and David Thomas, are in fact, fun: They would probably join you at Casa Bonita*. John and […]
A peek inside LMNL’s immersive experience.
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 […]
Our #1 fear: Withering under scrutiny.
Lucky me, I got to visit Onedome, an interactive arts and entertainment space on Market in San Francisco. Besides immersive installations, you can attend a cool 32-person F.E.A.S.T. dinner (Fine Dining, Entertainment, Art, Story & Technology). LMNL is a labyrinth offering 14 interactive rooms of digital art. I visited the LMNL exhibit: Stunning immersive visuals […]
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. […]