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Is A.I. better at avoiding bias?

The A.I. Journey podcast: Episode 01




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This podcast takes off with Jeroen and Ron talking about how algorithms can become biased and they discuss this on the basis of the gender bias hiring example. How can you avoid black box algorithms and force the neural network to represent its decision making process?

Next, they touch upon the accuracy of face and emotion recognition and how this relates to the 'dream' of Artificial General Intelligence (AGI). Can machines actually point into places where humans didn't go yet? (Spoiler: AlphaGo Zero) 

What can companies learn from this: who takes the responsibility to avoid bias and to have a balanced, unbiased data (training) set? Jeroen and Ron explain why Precision and Recall are a better metrics (over accuracy) to check whether your algorithm or data set is unbiased or not. And how can recommendation engines combined with post-processing help avoid collaborative filtering. 

Tune in to the Podcast: 

Citations
 

Ben Horowitz – “What you do is who you are” - https://a16z.com/book/whatyoudo/

Gary Marcus – Rebooting A.I. - http://garymarcus.com/

Hilo Aviezer - Contributions of facial expressions and body language to the rapid perception of dynamic emotions. - https://www.ncbi.nlm.nih.gov/pubmed/25964985

Nick Bostrom – “Superintelligence” - https://www.nickbostrom.com/views/superintelligence.pdf

Lee Jussim – Truth in Stereotypes - https://en.wikipedia.org/wiki/Lee_Jussim

Blendl – Dutch online news aggregator platform

Lex Fridman YouTube Algorithm Podcast - https://www.youtube.com/watch?v=nkWmiNRPU-c