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Patrick K. Lin has worked for a variety of public interest organizations, including the ACLU's Speech, Privacy & Technology Project, Federal Trade Commission, and Electronic Frontier Foundation. His research focuses on technology law and policy, artificial intelligence, surveillance, and privacy.
He is a frequent speaker on topics where technology, law, and social justice intersect. He has presented before a wide range of professional and academic groups, including Stanford University, Hofstra University, New York University, the New York County Law Association, Cloudflare TV, All Tech Is Human, and various podcasts.
Patrick received his J.D. from Brooklyn Law School and B.A. in Economics from New York University. While completing his law degree, he wrote an approachable and informative account of the intersection of technology, policy, and criminal justice in Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System.
Reviewed by K.C. Finn for Readers' Favorite
Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System is a work of non-fiction in the political subgenre. It is suitable for the general adult reading audience and was penned by author Patrick K. Lin. The book scrutinizes the machine-based aspects of the current criminal justice system and the biases that have been programmed into the automation at every level. From police utilization to DA decision-making and beyond, the issues with our thinking in the past have been set into the foundations of the supposedly objective systems currently in use.
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.
