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Algorithms for the People: Democracy in the Age of AI
How to put democracy at the heart of AI governance
Artificial intelligence and machine learning are reshaping our world. Police forces use them to decide where to send police officers, judges to decide whom to release on bail, welfare agencies to decide which children are at risk of abuse, and Facebook and Google to rank content and distribute ads. In these spheres, and many others, powerful prediction tools are changing how decisions are made, narrowing opportunities for the exercise of judgment, empathy, and creativity. In Algorithms for the People , Josh Simons flips the narrative about how we govern these technologies. Instead of examining the impact of technology on democracy, he explores how to put democracy at the heart of AI governance.
Drawing on his experience as a research fellow at Harvard University, a visiting research scientist on Facebook’s Responsible AI team, and a policy advisor to the UK’s Labour Party, Simons gets under the hood of predictive technologies, offering an accessible account of how they work, why they matter, and how to regulate the institutions that build and use them.
He argues that prediction is human choices about how to design and use predictive tools shape their effects. Approaching predictive technologies through the lens of political theory casts new light on how democracies should govern political choices made outside the sphere of representative politics. Showing the connection between technology regulation and democratic reform, Simons argues that we must go beyond conventional theorizing of AI ethics to wrestle with fundamental moral and political questions about how the governance of technology can support the flourishing of democracy.
Artificial intelligence and machine learning are reshaping our world. Police forces use them to decide where to send police officers, judges to decide whom to release on bail, welfare agencies to decide which children are at risk of abuse, and Facebook and Google to rank content and distribute ads. In these spheres, and many others, powerful prediction tools are changing how decisions are made, narrowing opportunities for the exercise of judgment, empathy, and creativity. In Algorithms for the People , Josh Simons flips the narrative about how we govern these technologies. Instead of examining the impact of technology on democracy, he explores how to put democracy at the heart of AI governance.
Drawing on his experience as a research fellow at Harvard University, a visiting research scientist on Facebook’s Responsible AI team, and a policy advisor to the UK’s Labour Party, Simons gets under the hood of predictive technologies, offering an accessible account of how they work, why they matter, and how to regulate the institutions that build and use them.
He argues that prediction is human choices about how to design and use predictive tools shape their effects. Approaching predictive technologies through the lens of political theory casts new light on how democracies should govern political choices made outside the sphere of representative politics. Showing the connection between technology regulation and democratic reform, Simons argues that we must go beyond conventional theorizing of AI ethics to wrestle with fundamental moral and political questions about how the governance of technology can support the flourishing of democracy.
320 pages, Hardcover
First published January 10, 2023
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Displaying 1 - 3 of 3 reviews
February 2, 2023
An easy way to write a bestseller in recent years is to attack algorithms that drive our lives through the largest tech companies' apps, products, and associated ads. It did not matter if all the suggestions of such books were utterly impractical as long as they succeeded in arousing the hackles by invoking the loss of privacy and choice.
Josh Simons' book is exactly the opposite and hence is unlikely to be a bestseller! The author approaches the topic pedantically with the writing style also of a career academician. The analysis is scholarly, nuanced, and result driven. The technical details, discussions of obscure philosophical points, and a tendency towards accepting the powers of some new-age forces cause the book challenging to accept for anyone looking for quick fixes or points for dinner party conversations. If one throws in some of the conclusions that argue against needless political and other interventions, the practicality and sagacity make it perfectly serious but so unsalacious that it is not a surprise this review is the first on the book's page weeks after its publication.
The following are reviewers' notes based on some of the lessons from the book and others completely independent. Technology continues to increase its influence on how we live. As much as it works in most cases, no amount of customization will make it work perfectly for all in all cases. Many commentators love imposing arbitrary limits on the workings of technology based on these negative outcomes regardless of the substantial cumulative benefits.
The critics' most strident issues are with the biases; the author mostly keeps the discussions to those more relevant in the US, but the arguments are universally applicable in different forms. Models trained on past patterns, the argument goes, are unlikely to help us move away from the plagues of race, religion, gender, wealth, and countless similar bigotries of our past.
The calls to eliminate the biases invariably turn to suggestions that the book shows will do more harm than good. Any forced removal of certain information from the models is unlikely to change the conclusions in a world where machine learning algorithms no longer work on human-created parameters or classifications. The results from algorithms may not be any different (or even worse) if the methods used are so simplistic.
The solutions are partly in assertive, outside-the-model actions that improve the prospects of the disadvantaged. The solutions are partly in the continuous monitoring of the end results by all concerned in critical life areas to permit suitable feedback loops. The solutions are in competition, and more use of technologies for higher granularity, fewer regulations on processes, judicious use of fines, simultaneous human monitoring, etc.
This is a serious book for those interested in the topic. The conclusions are not all I summarise here. They are granular and arrived at with many weighty arguments of all kinds.
Josh Simons' book is exactly the opposite and hence is unlikely to be a bestseller! The author approaches the topic pedantically with the writing style also of a career academician. The analysis is scholarly, nuanced, and result driven. The technical details, discussions of obscure philosophical points, and a tendency towards accepting the powers of some new-age forces cause the book challenging to accept for anyone looking for quick fixes or points for dinner party conversations. If one throws in some of the conclusions that argue against needless political and other interventions, the practicality and sagacity make it perfectly serious but so unsalacious that it is not a surprise this review is the first on the book's page weeks after its publication.
The following are reviewers' notes based on some of the lessons from the book and others completely independent. Technology continues to increase its influence on how we live. As much as it works in most cases, no amount of customization will make it work perfectly for all in all cases. Many commentators love imposing arbitrary limits on the workings of technology based on these negative outcomes regardless of the substantial cumulative benefits.
The critics' most strident issues are with the biases; the author mostly keeps the discussions to those more relevant in the US, but the arguments are universally applicable in different forms. Models trained on past patterns, the argument goes, are unlikely to help us move away from the plagues of race, religion, gender, wealth, and countless similar bigotries of our past.
The calls to eliminate the biases invariably turn to suggestions that the book shows will do more harm than good. Any forced removal of certain information from the models is unlikely to change the conclusions in a world where machine learning algorithms no longer work on human-created parameters or classifications. The results from algorithms may not be any different (or even worse) if the methods used are so simplistic.
The solutions are partly in assertive, outside-the-model actions that improve the prospects of the disadvantaged. The solutions are partly in the continuous monitoring of the end results by all concerned in critical life areas to permit suitable feedback loops. The solutions are in competition, and more use of technologies for higher granularity, fewer regulations on processes, judicious use of fines, simultaneous human monitoring, etc.
This is a serious book for those interested in the topic. The conclusions are not all I summarise here. They are granular and arrived at with many weighty arguments of all kinds.
December 18, 2023
It could be a great book, but it's just a bit dishonest. The author has an interesting solution in mind for regulating facebook and google and it's an interesting solution, whether I agree with it or not, I'd be interested in hearing about it, but not when he cloaks it in the guise of a book about algorithms, especially when he plays dumb in the first couple chapters about statistics and evaluation.
January 6, 2024
Good base, but repetitive argument, could have used more research to enlarge the core argument.
Displaying 1 - 3 of 3 reviews