Catherine ("Cathy") Helen O'Neil is an American mathematician and the author of the blog mathbabe.org and several books on data science, including Weapons of Math Destruction. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs.
She lives in New York City and is active in the Occupy movement.
A former Wall Street quant sounds an alarm on mathematical modeling—a pervasive new force in society that threatens to undermine democracy and widen inequality.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O’Neil reveals in this shocking book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his race or neighborhood), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.
Tracing the arc of a person’s life, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops. They don’t simply describe reality, as proponents claim, they change reality, by expanding or limiting the opportunities people have. O’Neil calls on modelers to take more responsibility for how their algorithms are being used. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
發表於2025-02-02
Weapons of Math Destruction 2025 pdf epub mobi 電子書 下載
感謝 recall 這本書的不知名同學,謝謝你逼得我用4個小時讀完。 作者創造瞭“數學殺傷性武器”(Weapons of Math Destruction, WMD)這個詞指代統計模型,探討現實生活中統計模型的大規模應用對社會的影響。 正麵例子是棒球、籃球比賽的分析,可以即時調整戰術(參考《點球成金...
評分感謝 recall 這本書的不知名同學,謝謝你逼得我用4個小時讀完。 作者創造瞭“數學殺傷性武器”(Weapons of Math Destruction, WMD)這個詞指代統計模型,探討現實生活中統計模型的大規模應用對社會的影響。 正麵例子是棒球、籃球比賽的分析,可以即時調整戰術(參考《點球成金...
評分文 / 董小琳 我們可以將時代劃分為:有大數據之前 和 有大數據之後。 為什麼要這麼分? 因為,誰也不能忽視,大數據對我們每個人生活方方麵麵的影響。 比如說: 之前,你的日子過得好不好,恐怕除瞭傢裏人,隻有幾個關係特彆好的朋友知道。 甚至,在親戚比較多的大傢庭裏,你還...
評分作者在華爾街對衝基金德紹集團擔任過金融工程師,後來去銀行做過風險分析,再後來去做旅遊網站的用戶分析。後來辭職專門揭露美國社會生活背後的各種算法的陰暗麵。 書中提到的算法的技術缺陷,我歸納為兩點:第一個比較緻命:不準確。不準確有兩種體現,首先是算法先天的問題,...
評分感謝 recall 這本書的不知名同學,謝謝你逼得我用4個小時讀完。 作者創造瞭“數學殺傷性武器”(Weapons of Math Destruction, WMD)這個詞指代統計模型,探討現實生活中統計模型的大規模應用對社會的影響。 正麵例子是棒球、籃球比賽的分析,可以即時調整戰術(參考《點球成金...
圖書標籤: 大數據 社會學 美國 數字社會學 inequality 數學 社會 政治科學
中國急需這樣的左翼知識分子:對技術有深刻理解,並且能看到技術對社會造成的影響。
評分一篇討伐大數據的檄文。與那些贊歌不同,作者解釋各行各業中所用的數學模型(以及人們應對這些模型的方法)背後所蘊藏的種種歧視、黑箱與不公。這些陰暗麵加劇瞭當今社會的貧富差距和底層人民的憤怒,監管時不我待。
評分大數據模型在參數選擇上的任意,數據統計上的不科學,模型適用的不科學推廣,導緻大數據模型在招生就業犯罪和選舉問題上的不公正和不平等。雖然都是舉例,但介紹瞭數據對人生活加以掌控的方方麵麵。
評分中國急需這樣的左翼知識分子:對技術有深刻理解,並且能看到技術對社會造成的影響。
評分可能之前期待值太高 所以落差比較大.. 對fairness and accountability in ml比較陌生的人還是很推薦的。 讀起來覺得大媽強項的數學模型方麵可能考慮非technical讀者粗略帶過不過癮, 不是專項的policy方麵argument又比較sloppy...
Weapons of Math Destruction 2025 pdf epub mobi 電子書 下載