“. . . for AI, we need to have more comprehensive governance that includes everything from ethics to explainability to accountability to bringing together all of the operational side of a model. Is it working properly? Is it being retrained continuously? Is it getting better? Does it need to be retired? But also, wedding that to some of these broader customer-relevant and employee-relevant issues.” [15:17]
“ . . . what AI is trying to do using probability is creating new conundrums that the business isn’t used to dealing with. And people aren’t very good at probabilistic thinking in the first place. So I think unfortunately, it’s one of these cases where you’ve got to bring everyone together to do this internally. My colleague Michele Goetz and I did a whole bunch of interviews around this. And we found that even some really large, sophisticated technology companies haven’t mastered every dimension of AI governance.” [16:09]
A human-centered view of artificial intelligence [9:21]
The coming decade of job loss, job gains, and job transformation [24:49]
Gownder’s science fiction recommendations, which tackle inequality, climate change, and AI [27:24]
“Most adults don’t know how machine learning works, how the face recognition on the phone works, what’s behind all of this. And that’s the idea -- kind of unpacking this black box of AI technologies because it’s everywhere.
“When you don’t know something, it can be very scary. You don’t know what the implications are and so you tend to sort of shut it down even more, and you’re resistant to any kind of re-skilling, up-skilling, which will have to happen no matter what.” [3:44]
Other Episode Highlights
A Focus on Grassroots AI [6:34]
How Technovation Demystifies AI [24:39]
The Technovation “Trick”: Adults and Kids Learn AI Together [26:51]
“I definitely think we need a public conversation about what our expectations are for AI. So it’s not just ‘I want more accurate AI.’ It’s do we want AI doing particular things? We need to bring together, I would say, people from different walks of life.
“So you’ve got your scientists that we all think about, but also your ethicists and your business leaders, your citizens, your policymakers, and have more of a joint dialogue about what we want from AI systems. They are different than software we’ve had in the past because of the reach and the power that they really have the ability to amplify the best and the worst of what we can do.” [20:40]
Other Episode Highlights
Striking Examples of AI Failure [5:10]
Why Talking to Data Scientists Gives Her Hope [18:45]