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This is: [Book Review] "The Alignment Problem", published by Brian Christian,Lsusr on the AI Alignment Forum.
I came to this book with an ax to grind. I combed through page after page for factual errors, minor misrepresentations or even just contestable opinions. I spotted (what seemed like) omission after omission only to be frustrated just a few pages later when Brian Christian addressed them. In the Chapter 5: Shaping I thought I found a major mistake. Brian Christian addressed Skinnerian operant conditioning without addressing the real way we manages human groups: leading by example.
That's because he dedicated all of Chapter 7: Imitation to the subject. Thus, through gritted teeth, I reluctantly acknowledge that The Alignment Problem by Brian Christian is a fantastic book in all respects.
Despite my best efforts, Brian Christian even taught me lots of cool things about state-of-the-art machine learning. The Alignment Problem addresses advanced technical problems while being readable to non-technical people. This book would be a useful read both for activists who want to better understand public policy AND for aspiring engineers who want to get up to speed with machine learning. The only possible fault I can imagine with this book is that, since it depends so heavily on cutting-edge research, it might be rendered obsolete in a decade or two. Much of it mirrors the actual technical work I'm doing in machine learning.
The book starts with practical real-world problem that are happening right now. Most of the book is dedicated to explaining machine learning problems and their solutions. At the end it extrapolates on to the choices machine learning creates for our future.
Racist Machines
In 2015 Google famously released an image classifier that labelled Black people as "gorillas" because there were so few Black people in its training dataset. The good solution is to add more Black people to the training dataset. The fast solution is to keep using a biased algorithm and just cover up the most egregious errors. I don't know which approach Google went with but "three years later, in 2018, Wired reported that the label 'gorilla' was still manually deactivated in Google Photos."
I'm curious what animal I would get classified as if people who look like me were removed from Google Photos training dataset. (I hope it's a meerkat.) Alas, computer algorithms are already being used to make much more consequential decisions. Many of these decisions involve problems that can't be solved just by collecting more training data.
The Mathematics of Social Justice
[This section refers to situations in the United States unless otherwise noted.]
Black people and White people self-report similar marijuana usage. However, Black people are arrested for marijuana usage much more frequently than White people. Suppose you are designing an algorithm to determine how much to punish a prisoner for smoking marijuana. If you ignore the prisoner's race then you will inflict penalties several times harsher on Black people than on White people. Race blindness produces racist outcomes.
Suppose we factor in race to create fair outcomes on average. A White citizen arrested for smoking pot gets punished 7× per use compared to a Black citizen. This is unfair to White people who are now treated harsher on the basis of skin color. We are balancing two mutually-exclusive values. Either we can punish Black people unfairly and/or we can punish White people unfairly. We cannot be simultaneously fair to both groups.
The solution to the above problem is "stop arresting Black people disproportionately for the same crime" but that only solves the problem for crimes where different races have the same base rates. What happens when Black people actually do commit more crimes?
Suppose we're designing a system to decide which co...
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