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…
Reading was a childhood passion of mine. My mother was a librarian and got me interested in reading early in life. When John F. Kennedy was running for president and after his assassination, I became intensely interested in politics. In addition to reading history and political biographies, I consumed newspapers and television news. It is this background that I have drawn upon over the decades that has added value to my research.
It didn’t begin with Donald Trump. When the Republican Party lost five straight presidential elections during the 1930s and 1940s, three things happened: (1) Republicans came to believe that presidential elections are rigged; (2) Conspiracy theories arose and were believed; and (3) The presidency was elevated to cult-like status.
Long before Trump, each of these phenomena grew in importance. The John Birch Society and McCarthyism became powerful forces; Dwight D. Eisenhower was the first “personal president” to rise above the party; and the development of what Harry Truman called “the big lie,” where outrageous falsehoods came to be believed. Trump…
Grand Old Unraveling: The Republican Party, Donald Trump, and the Rise of Authoritarianism
It didn't begin with Donald Trump. The unraveling of the Grand Old Party has been decades in the making. Since the time of FDR, the Republican Party has been home to conspiracy thinking, including a belief that lost elections were rigged. And when Republicans later won the White House, the party elevated their presidents to heroic status-a predisposition that eventually posed a threat to democracy. Building on his esteemed 2016 book, What Happened to the Republican Party?, John Kenneth White proposes to explain why this happened-not just the election of Trump but the authoritarian shift in the party as a…
