Auteurs similaires à suivre
Gérer vos suivis
Les clients ont aussi acheté des articles de
Mises à jour de l'auteur
Livres de Cathy O'Neil
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for 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.
And yet, as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and incontestable, even when they're wrong. Most troubling, they reinforce discrimination. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort CVs, grant or deny loans, evaluate workers, target voters, and monitor our health.
O'Neil calls on modellers to take more responsibility for their algorithms and on policy makers to regulate their use. 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.
“O’Neil reminds us that we must resist the urge to judge, belittle, and oversimplify, and instead allow always for complexity and lead always with empathy.”—Dave Eggers, author of The Every
Shame is a powerful and sometimes useful tool: When we publicly shame corrupt politicians, abusive celebrities, or predatory corporations, we reinforce values of fairness and justice. But as Cathy O’Neil argues in this revelatory book, shaming has taken a new and dangerous turn. It is increasingly being weaponized—used as a way to shift responsibility for social problems from institutions to individuals. Shaming children for not being able to afford school lunches or adults for not being able to find work lets us off the hook as a society. After all, why pay higher taxes to fund programs for people who are fundamentally unworthy?
O’Neil explores the machinery behind all this shame, showing how governments, corporations, and the healthcare system capitalize on it. There are damning stories of rehab clinics, reentry programs, drug and diet companies, and social media platforms—all of which profit from “punching down” on the vulnerable. Woven throughout The Shame Machine is the story of O’Neil’s own struggle with body image and her recent weight-loss surgery, which awakened her to the systematic shaming of fat people seeking medical care.
With clarity and nuance, O’Neil dissects the relationship between shame and power. Whom does the system serve? Is it counter-productive to call out racists, misogynists, and vaccine skeptics? If so, when should someone be “canceled”? How do current incentive structures perpetuate the shaming cycle? And, most important, how can we all fight back?
Qui choisit votre université ? Qui vous accorde un crédit, une assurance, et sélectionne vos professeurs ? Qui influence votre vote aux élections ? Ce sont des formules mathématiques.
Ancienne analyste à Wall Street devenue une figure majeure de la lutte contre les dérives des algorithmes, Cathy O'Neil dévoile ces " armes de destruction mathématiques " qui se développent grâce à l'ultra-connexion et leur puissance de calcul exponentielle. Brillante mathématicienne, elle explique avec une simplicité percutante comment les algorithmes font le jeu du profit.
Cet ouvrage fait le tour du monde depuis sa parution. Il explore des domaines aussi variés que l'emploi, l'éducation, la politique, nos habitudes de consommation. Nous ne pouvons plus ignorer les dérives croissantes d'une industrie des données qui favorise les inégalités et continue d'échapper à tout contrôle. Voulons-nous que ces formules mathématiques décident à notre place ? C'est un débat essentiel, au cœur de la démocratie.
O'Neil expone los modelos que dan forma a nuestro futuro, como individuos y como sociedad. Estas "armas de destrucción matemática" califican a maestros y estudiantes, ordenan currículos, conceden (o niegan) préstamos, evalúan a los trabajadores, se dirigen a los votantes, fijan la libertad condicional y monitorean nuestra salud.
Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
- Statistical inference, exploratory data analysis, and the data science process
- Spam filters, Naive Bayes, and data wrangling
- Logistic regression
- Financial modeling
- Recommendation engines and causality
- Data visualization
- Social networks and data journalism
- Data engineering, MapReduce, Pregel, and Hadoop
Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Lungi dall'essere modelli matematici oggettivi e trasparenti, gli algoritmi che ormai dominano la nostra quotidianità iperconnessa sono spesso vere e proprie armi di distruzione matematica: non tengono conto di variabili fondamentali, incorporano pregiudizi e se sbagliano non offrono possibilità di appello. Queste armi pericolose giudicano insegnanti e studenti, vagliano curricula, stabiliscono se concedere o negare prestiti, valutano l’operato dei lavoratori, influenzano gli elettori, monitorano la nostra salute. Basandosi su case studies nei campi più disparati ma che appartengono alla vita di ognuno di noi, Cathy O’Neil espone i rischi della discriminazione algoritmica a favore di modelli matematici più equi ed etici, perché "la matematica merita ben altro di queste armi di distruzione, e altrettanto la democrazia".