A few weeks ago I had a conversation with
Now that algorithms are everywhere, helping us to both run and make sense of the world, a strange question has emerged among artificial intelligence researchers: When is it ok to predict the future based on the past? When is it ok to be biased?
“I want a machine-learning algorithm to learn what tumors looked like in the past, and I want it to become biased toward selecting those kind of tumors in the future,” explains philosopher Shannon Vallor at Santa Clara University. “But I don’t want a machine-learning algorithm to learn what successful engineers and doctors looked like in the past and then become biased toward selecting those kinds of people when sorting and ranking resumes.”
We talk about this, sentencing algorithms, the notion of how to raise and teach our digital offspring, and more. You can listen to all it here:
If and when it gets a transcript, I will update this post with a link to that.
Until Next Time.