Cognitive Biases 101, with Peter Baumann | Big Think
Cognitive Biases 101, with Peter Baumann
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How much does cognitive bias change people's perception? Well, the history of computing would be a lot different. And so would many major orchestras, who had to implement a curtain during auditions so that judges and orchestral directors could only judge musicians on their skills... and not their gender. Michael Li, PhD, is the founder of The Data Incubator, an education startup training STEM PhDs to be data scientists and quants.
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MICHAEL LI:
Michael Li, PhD, is the founder of The Data Incubator, an education startup training STEM PhDs to be data scientists and quants. He has worked at Foursquare, Google, A16Z, NASA, JPMorgan, and D.E. Shaw. He is a Hertz, NSF, and Marshall Scholar and is an alumnus of Princeton and Cambridge. Michael founded a student-run conference in quant trading that features leading industry figures and raises money for educational non-profits like America Needs You.
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TRANSCRIPT:
Michael Li: A sort of interesting fact is that, while today programming is viewed as an extremely male-dominated field, it was totally the opposite at the dawn of computing.
So if you look at who the original programmers were, they were actually women! All programmers from the very beginning were women and it was because this job was seen as being “beneath” men. And so somehow in the interceding 30, 40, 50 years, that gender of dynamics has completely shifted around.
But what we’re seeing now is that sometimes it’s the implicit biases that we have which are holding back women and minorities from entering the workforce, either as data scientists or as computer engineers and software engineers.
And we’ve seen a lot of research in this area that’s shown that there can be some implicit biases in how we judge people once we know their name, their gender or their race.
And what we do when we assess the people who are going to be working for us is we are completely blind to these things.
We actually strip away the name when we consider people’s applications. We just look at how they perform on a series of challenges that we give them that really try to test their ability to be data scientists and test their understanding of these kind of core fundamental mathematical programming concepts.
And when we do that I think it actually becomes a much more fair process and it actually can help increase the number of women and underrepresented minorities who sort of make it through the screening process.
Just to give you one sort of quick anecdote about this there’s a famous story about music auditions in the 1970s where orchestras had a very, very tiny percentage of their members or their players there – the people who were playing in the orchestra as women.
And what happened is at some point they decided to try to break free from this and they would put down a curtain between the performer, that is the auditioner, and the judging panel that was trying to determine whether she or he should be allowed to play in the orchestra. And when they did the results were night and day.
There’s a famous study that’s up on the National Bureau of Economic Research’s website published by two famous researchers from Harvard talking about this.
It’s called “orchestrating diversity” and it talks about how the results were a night and day difference: the fraction for women who made it past the screening round shot up something like sevenfold between not having the curtain down and having the curtain down.
And it just goes to sort of show that at this time there was an implicit bias that women weren’t really the kind of caliber of musician that you needed to be able to perform at Carnegie Hall, right? At these kind of top level symphonic performance.
And when you put down a curtain and you just listened to them as opposed to being able to see whether they were a man or a woman, you then—without that kind of knowledge you suddenly were forced to make judgments just based on the music, just based on their ability and you saw that you were much more willing to let in women than before.
