商品簡介
★ 前華爾街量化分析師揭露：演算法如何操控你的未來
★ 大數據黑暗面全面曝光：看不見的歧視與不公平
我們以為世界正變得更公平——由機器取代人類做決策，似乎能消除偏見。但真相，可能正好相反。在這個「演算法主宰」的時代，你能否錄取學校、拿到工作、申請貸款，甚至保險費率高低，都悄悄被一套看不見的數學模型決定。這些系統看似客觀，卻無法被檢驗，也無從質疑，一旦出錯，影響卻會層層放大。數學家暨資料科學家凱西．歐尼爾，以親身經驗揭開大數據的陰暗面：這些模型不只沒有消除不平等，反而強化既有的階級與歧視，讓弱勢更難翻身。本書如同一場警醒之旅，帶你看見科技背後不為人知的權力運作。
NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword
“A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times
NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point
We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.
But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.
— Longlist for National Book Award (Non-Fiction)
— Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology)
— Kirkus, Best Books of 2016
— New York Times, 100 Notable Books of 2016 (Non-Fiction)
— The Guardian, Best Books of 2016
— WBUR's "On Point," Best Books of 2016: Staff Picks
— Boston Globe, Best Books of 2016, Non-Fiction
作者簡介
Cathy O'Neil is a data scientist and author of the blog mathbabe.org. She earned a Ph.D. in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D. E. Shaw. She then worked as a data scientist at various start-ups, building models that predict people’s purchases and clicks. O’Neil started the Lede Program in Data Journalism at Columbia and is the author of Doing Data Science. She is currently a columnist for Bloomberg View.
