My personal passion behind ethical AI started early in my life. I was raised by someone who had a personality disorder, and grew up being gaslit and manipulated. It was hard for me personally to understand what was reality and what was made up. Being a nerdy kid, I spent most of my time studying computers and math to escape it all. And while I have made my own life writing books on machine learning, and programming for a living, I also care deeply about how what I do affects others. Being thoughtful is deep within me, and I sit with a Zen group and volunteer with the Mankind Project.
I wrote...
Thoughtful Machine Learning with Python: A Test-Driven Approach
Thoughtful Machine Learning with Python is a book that details how to build models using care with test-driven development (TDD). TDD is one method for testing your assumptions and a way to step towards the ethical use of models.
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.
From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff's lively and engaging primer clarifies the basic principles of statistics and explains how they're used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.
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.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
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.
Everyone wants to succeed in life. But what causes some of us to be more successful than others? Is it really down to skill and strategy - or something altogether more unpredictable?
This book is the bestselling sensation that will change the way you think about business and the world. It is all about luck: more precisely, how we perceive luck in our personal and professional experiences. Nowhere is this more obvious than in the markets - we hear an entrepreneur has 'vision' or a trader is 'talented', but all too often their performance is down to chance rather than…
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.
When published in 1981, The Mismeasure of Man was immediately hailed as a masterwork, the ringing answer to those who would classify people, rank them according to their supposed genetic gifts and limits.
And yet the idea of innate limits-of biology as destiny-dies hard, as witness the attention devoted to The Bell Curve, whose arguments are here so effectively anticipated and thoroughly undermined by Stephen Jay Gould. In this edition Dr. Gould has written a substantial new introduction telling how and why he wrote the book and tracing the subsequent history of the controversy on innateness right through The Bell…
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.
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role…
Benghazi: A New History is a look back at the enigmatic 2012 attack on the US mission in Benghazi, Libya, its long-tail causes, and devastating (and largely unexamined) consequences for US domestic politics and foreign policy. It contains information not found elsewhere, and is backed up by 40 pages of citations and interviews with more than 250 key protagonists, experts, and witnesses.
So far, the book is the main -- and only -- antidote to a slew of early partisan “Benghazi” polemics, and the first to put the attack in its longer term historical, political, and social context. If you want to understand some of the events that have shaped present-day America, from political polarization and the election of Donald Trump, to January 6, the US withdrawal from Afghanistan, Russian expansionism, and the current Israel-Hamas war, I argue, you need to understand some of the twists and turns of America's most infamous "non-scandal, scandal.”
I was in Benghazi well before, during, and after the attack as a US diplomat and co-director of a medical NGO. I have written three books, and have been a contributor to The NYT, Foreign Affairs, Forbes, Salon, The Financial Times, Newsweek, and others.
On September 11, 2012, Al Qaeda proxies attacked and set fire to the US mission in Benghazi, Libya, killing a US Ambassador and three other Americans. The attack launched one of the longest and most consequential 'scandals' in US history, only to disappear from public view once its political value was spent.
Written in a highly engaging narrative style by one of a few Western experts on Libya, and decidely non-partisan, Benghazi!: A New History is the first to provide the full context for an event that divided, incited, and baffled most of America for more than three years, while silently reshaping…
