Overview

"""When today's technology relies on yesterday's data, it will simply mirror our past mistakes and biases."" AI and other high-tech tools embed and reinforce America's history of prejudice and exclusion - even when they are used with the best intentions. Patrick K. Lin's Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System takes a deep and thorough look into the use of technology in the criminal justice system, and investigates the instances of coded bias present at every level. In this book, you'll learn how algorithms and high-tech tools are used in unexpected ways: suggesting which neighborhoods to police, predicting whether someone is more or less likely to commit a crime, and determining how long someone's prison sentence should be. Machine See, Machine Do takes you on an eye-opening journey of discovery, encouraging you to think twice about our current system of justice and the technology that supposedly makes it more ""objective"" and ""fair."" If you are someone who cares deeply about criminal justice reform, is curious about the role of technology in our day-to-day lives, and ultimately believes we should aspire to make both of these spaces more ethical and safe, this book is for you."

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9781637308219

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Patrick K. Lin has the rare ability to explain complex technical matters in plain English, and his book is a valuable contribution to the literature of AI. As Lin warns, unless we ask the right questions right now, AI is more likely to replicate the problems of yesterday than to create the solutions for tomorrow. - Ben Wizner, Director of the ACLU's Speech, Privacy & Technology Project If you care about the future of our civil liberties, you must also care about AI and algorithms that are being developed and deployed today. Machine See, Machine Do is a clarion call to recognize the serious consequences of treating technology as neutral and ensure that the speed of AI does not outstrip our ability to control its impact. Lin does a masterful job of demystifying the overpromise of AI and folly of tech solutionism in the criminal justice system, while centering the tech conversation on where it should be: our humanity. - David Ryan Polgar, Founder and Director of All Tech Is Human Our technology reflects our priorities. That is Patrick K. Lin's message as he educates us about the human relationship with technology throughout history. Machine See, Machine Do reminds us that we must evaluate the purpose of AI when we are the data that trains it. - Sudha Jamthe, Instructor at Stanford University Continuing Studies and Author of AIX: Designing Artificial Intelligence

Patrick K. Lin has the rare ability to explain complex technical matters in plain English, and his book is a valuable contribution to the literature of AI. As Lin warns, unless we ask the right questions right now, AI is more likely to replicate the problems of yesterday than to create the solutions for tomorrow. - Ben Wizner, Director of the ACLU's Speech, Privacy & Technology Project If you care about the future of our civil liberties, you must also care about AI and algorithms that are being developed and deployed today. Machine See, Machine Do is a clarion call to recognize the serious consequences of treating technology as neutral and ensure that the speed of AI does not outstrip our ability to control its impact. Lin does a masterful job of demystifying the overpromise of AI and folly of tech solutionism in the criminal justice system, while centering the tech conversation on where it should be: our humanity. - David Ryan Polgar, Founder and Director of All Tech Is Human Our technology reflects our priorities. That is Patrick K. Lin's message as he educates us about the human relationship with technology throughout history. Machine See, Machine Do reminds us that we must evaluate the purpose of AI when we are the data that trains it. This is a must-read book you will go back to again and again. - Sudha Jamthe, Instructor at Stanford University Continuing Studies and Author of AIX: Designing Artificial Intelligence There's nothing quite like reading a non-fiction book written by a passionate expert, and Patrick K. Lin certainly fits this description; breaking down the technical matters into understandable pieces, consistently demonstrating and exploring the crux of the complex issue, and writing with a view to educate and inform change. As a layman on the details of computer programming, I appreciated the author's natural ability to break the technical side of things down to an accessible level in order to focus on the social and philosophical issues at stake. This is an important book in its genre with serious warnings about the implications of current AI application on our civil liberties, with frequent moments to discuss the wider societal issues that have been inadvertently embedded in the things we've built. Overall, Machine See, Machine Do offers constant reminders that if we stop reflecting on ourselves and the information we feed into our machines as they learn about the world, then we risk amplifying the problems that we currently face, especially with society-wide issues such as law and order. - K.C. Finn, Reviewer at Readers' Favorite Machine See, Machine Do is a captivating and thoughtful introduction to the intersection of algorithmic bias and criminal justice. Lin has crafted an approachable and clarifying guide by interviewing experts and sharing personal anecdotes. Machine See, Machine Do focuses on the criminal justice system, but its lessons and warnings are applicable to other spaces, such as hiring and college admissions, just two hotbeds of scrutiny and activity today. This book is a must-read. - Sang Lee, Co-Founder and CEO of Thine

Author Information

. is an attorney and researcher focused on AI, privacy, and technology regulation. He is the author of , , a book that explores the ways public institutions use technology to surveil, police, and make decisions about the public, as well as the historical biases that impact that technology.Patrick has extensive experience in litigation and policy, having worked for the ACLU, FTC, EFF, and other organizations that advocate for digital rights and social justice. He is passionate about addressing the ethical and legal challenges posed by emerging technologies, especially in the areas of surveillance, algorithmic bias, and data privacy. He serves as the junior board chair of the Surveillance Technology Oversight Project (STOP). He has also published multiple articles and papers on topics such as facial recognition, data protection, and copyright law.

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