From the Bookshelf of Science and Inquiry…

The Alignment Problem: Machine Learning and Human Values

Brian Christian

Start date: March 1, 2022

Finish date: March 31, 2022

Discussion: Book Club 2022

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Book Club 2022

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Jan 23, 2022 David Rubenstein rated it it was amazing

Shelves: computers, philosophy, sociology, psychology, technology

The biggest problem in artificial intelligence (AI) is to devise a reward function that gives you the behavior you want, while avoiding side effects or unforseen consequences. This book examines the alignment problem from a number of fascinating perspectives.

This is a fascinating book, full of the implications of AI on philosophy, sociology, and psychology. There are interactions between AI and sociology, psychology in a two-way street. Our understanding of psychology helps to improve AI in num The biggest problem in artificial intelligence (AI) is to devise a reward function that gives you the behavior you want, while avoiding side effects or unforseen consequences. This book examines the alignment problem from a number of fascinating perspectives.

This is a fascinating book, full of the implications of AI on philosophy, sociology, and psychology. There are interactions between AI and sociology, psychology in a two-way street. Our understanding of psychology helps to improve AI in numerous ways. Also, AI gives researchers many valuable insights into psychology, and issues in sociology. After all, we want automated algorithms to be unbiased, to be fair. But, who is to say exactly what is fair? Sometimes, the answer isn't easy.

The first problem, well known to workers in AI, is the inherent bias due to small training datasets. AI algorithms demonstrate bias, and can subtly perpetuate it. It seems like many of the biases are not the fault of the algorithms, but instead are a mirror of society and culture. In the 1950's, people tried to predict, using punch card machiles, which prisoners would succeed on parole. A ProPublica study was conducted of the accuracy of COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). COMPAS is used to predict whether an inmate, if released, would commit a violent or a nonviolent crime within 1-3 years. The algorithm was found to be biased against blacks; it overpredicts recidivism among blacks, and underpredicts for whites. A key factor is that it actually does not predict whether a released prisoner would commit a crime. It really predicts whether a released prisoner would be arrested and convicted for a crime. Higher rates of police profiling blacks lead to an inherent bias.

There is a US antidiscrimination law that prohibits certain attributes--like race and gender--from being used in machine-learning modes for hiring, criminal detentions, and so on. Nevertheless, other unprotected variables are correlated with race and gender, so the algorithms can still be discriminatory. In addition, the blocking of these attributes prevents or even mitigating the discrimination!

Predicting whether of not a patient with pneumonia should be hospitalized as an inpatient is problematic. Models predict that if a patient has a chest pain, or has heart disease, asthma, or is over 100, then the patient is less likely to die! The reason is that patients with these conditions automatically receive more care, so they are less likely to die.

Many problems in AI are solved by looking at psychology. For example, BF Skinner taught a pigeon how to bowl in a miniature alley through incremental steps. This led researchers to teach an algorithm to play difficult video games by rewarding incremental steps. Basically, great video games train you how to play. Similarly, neural networks learn language translation by starting with simple sentences before graduating to more difficult ones. This approach is similar to language learning by children. The book Bobby Fischer Teaches Chess uses a similar approach.

AI is not just about automating tasks, but how can we better understand human psychology. How can we best train ourselves/ First, we should use sparse rewards. Second, we should incentivize a state, not an action. In real life, we can use gamification as an approach to reinforcement learning. Studies of toddlers show that toys that seem to violate the laws of physics were most novel, and held the interest of six-year-olds for the longest time. Infants use violations of prior expectations as special opportunities for learning.

Psychologists have studied overimitation in children and chimpanzees. People learning a new task will learn best through imitation. Sometimes we imitate behaviors that are not relevant to a task. A toddler might overimitate if he cannot figure out why an adult is doing something, so he does it too. As it turns out, chimpanzees do not purposely overimitate. But children can understand whether an adult is teaching or simply experimenting. If an adult is experimenting, the child does not overimitate.

A fascinating chapter of imitation describes the problems encountered by the the first researchers in autonomous driving. Teaching an autonomous care in a video game to drive with imitation is best done by randomly alternating between human and machine drivers.

This book is fascinating on many levels. But it is not always an easy read. Some of the concepts are difficult, even subtle. It is such a pleasure to read a well-researched book that plumbs to the depths of a complicated subject. ...more

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Apr 08, 2022 aPriL does feral sometimes rated it it was amazing · review of another edition

Shelves: favorites, science, academic-notations, non-fiction, twisted-me-peculiar

'The Alignment Problem: Machine Learning and Human Values' by Brian Christian is a very interesting overview about the issues in developing useful computing machines. I found it very comprehensive and yet easy to understand. However, it does give me pause in any fantasy I may have had over the Singularity occurring.

The main goal of machine learning is teaching the computer to see, hear and do things without human oversight, and to learn to categorize and make inferences on inputs like humans, an ...more

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Mar 09, 2024 taylor rated it liked it

A hard one to judge for me. Some parts were fantastic, in fact I looked up papers referenced as they may have immediate impact for my work. Other parts seem to drift into more behavioral studies. A bit disappointed that there was no deep discussion on actually how to align AI goals and rewards with what mankind wants. Perhaps because there is no single vector in which all of mankind wants. In future we will electing not people but maybe strategies or goals to be used by our AI overlord president ...more

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