Feeding the Machine
The Hidden Human Labor Powering A.I.
-
- ¥3,400
発行者による作品情報
For readers of Naomi Klein and Nicole Perlroth, a myth-dissolving exposé of how artificial intelligence exploits human labor, and a resounding argument for a more equitable digital future.
Silicon Valley has sold us the illusion that artificial intelligence is a frictionless technology that will bring wealth and prosperity to humanity. But hidden beneath this smooth surface lies the grim reality of a precarious global workforce of millions laboring under often appalling conditions to make A.I. possible. This book presents an urgent, riveting investigation of the intricate network that maintains this exploitative system, revealing the untold truth of A.I.
Based on hundreds of interviews and thousands of hours of fieldwork over more than a decade, Feeding the Machine describes the lives of the workers deliberately concealed from view, and the power structures that determine their future. It gives voice to the people whom A.I. exploits, from accomplished writers and artists to the armies of data annotators, content moderators and warehouse workers, revealing how their dangerous, low-paid labor is connected to longer histories of gendered, racialized, and colonial exploitation.
A.I. is an extraction machine that feeds off humanity's collective effort and intelligence, churning through ever-larger datasets to power its algorithms. This book is a call to arms that details what we need to do to fight for a more just digital future.
PUBLISHERS WEEKLY
AI depends on the exploitation of artists, data annotators, and engineers, among others, according to this damning exposé. Graham (coauthor of The Digital Continent), an internet geography professor at the Oxford Internet Institute, teams up with Muldoon (Platform Socialism) and Cant (Riding for Deliveroo), both management professors at the University of Essex, to spotlight individuals whose labor powers AI. The authors describe the travails of a data annotator from rural Uganda who, for the equivalent of per hour, works grueling shifts marking traffic lights, human faces, and other elements of interest in images that tech companies use to train software. Ethics have been sidelined in the AI gold rush, the authors contend, discussing a London machine learning engineer's concern over the fact that her employer's lack of guidance on handling sensitive topics when training the technology (e.g., "Should a particular event be described as a genocide?") leaves often poorly paid data annotators to make morally freighted decisions. The grim real-life stories read like dystopian parables, such as the account of a European voice actor whose recordings were legally used without her consent to create an inexpensive synthetic clone whom she now competes with for business. Driven by striking reporting and finely observed profiles, this unsettles.
