Nobel Laureate and center namesake Daniel Kahneman spoke to a packed house of students on Tuesday, October 17. Reflecting on the last decade of consulting work he has done with companies since the publishing of Thinking, Fast and Slow, he reminds us that bias and noise are independent and shouldn't be confused. Error, however, is additive, so if you can reduce the noise--which is easier to control than the bias--you reduce overall error. As such, Kahneman posits, we are spending too much time emphasizing bias and not enough on noise. He suggests we look to algorithms to address this issue, as they are definitionally noise-free.
Nathan Matias, a Postdoctoral Fellow in the Paluck Lab, liveblogged the talk. His summary can be found on medium.com.