The best books about ethical artificial intelligence

How to Lie with Statistics is a book that I highly recommend to anybody just starting out in data science. While we would like to believe that data science is a science many times it’s not, it’s storytelling. This storytelling with data can quickly get us into trouble. Whether it’s shortening y-axis or presenting data in a way that makes things look better than they are.

Personally I have found this book to be invaluable especially when working with business leaders as to why I won’t do certain things to my models and presentations.

More power means more responsibility. Let’s face it, machine learning, artificial intelligence, and data science tend to make algorithms much more pervasive. This pervasiveness is great, although it comes with a secondary effect of causing other issues. Whether it’s policing, racial profiling, or other sticky issues math and statistics tend to not care.

It’s up to us to make sure we use math for good. That’s why I recommend this book.

Fooled by Randomness is one of the best books I have ever read on the trouble of working with real-world data. Many times we think that real-world data is easy to work with but it’s full of noise instead. But what kind of noise can quickly get us into trouble. In this book, Taleb goes into detail about common traps and pitfalls to avoid so that we don’t re-create the 2008 financial crash again.

I’ll be honest, I never took tests well. And I wasn’t accepted to my first pick of a high school. That is why I recommend this book. Many times we think that evaluative tests, like the IQ test, determine how intelligent someone is. And that just isn’t true. In this book by Stephen Jay Gould he goes into detail about the issues with ranking criteria on human intelligence and how we just can’t do it in practice.

Peter Flach’s book on machine learning had a profound impact on me. The book is simple to understand, and highly visual. But beyond that Peter himself is a lovely person who obviously cares about all his students. I believe for getting started in machine learning and wanting to understand the algorithms that power many models, this is a great place to start.

But most importantly it’s a great way to understand the power and gain more intention behind what we are doing.