testing
5 stars
I did read this book, and it was very good, however this is primarily a test post
Paperback, 259 pages
English language
Published Nov. 16, 2016 by Allen Lane.
Weapons of math destruction
how big data increases inequality and threatens democracy
by Cathy O'Neil
4 stars
(13 reviews)
A former Wall Street quant 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 …
A former Wall Street quant 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.
Suit up for Combat!
5 stars
This was an exceptional book. It's not heavy into statistics but gives the rationale for what is a WMD (Weapon of Math Destruction) and WMDs maybe a new term but we have been under the exploitation of WMDs well before we think. It's not a new phenomenon but it is one that we should be aware of.
Take a read and learn how about them so that we can all do better to combat them and use math to not only help describe the world but make it a better place to live in.
SocProf reviewed Weapons of Math Destruction by Cathy O'Neil
Review of 'Weapons of Math Destruction' on 'Goodreads'
5 stars
A very timely, detailed, and expert (yet highly readable) look at the damaging potentials of using algorithms to regulate our lives. This absolutely relevant book is a must-read for anyone involved in data science or simply algorithm users.
Using a clear definition of what constitutes WMDs, (opacity, negative feedback loops, no absence of feedback, they tend to punish the poor, model = black box). O'Neil goes through a wide range of social institutions (health, education, work, politics, criminal justice, to name a few) and examines the damages done by WMDs. My own personal distaste for work wellness stuff felt very validated.
In the end, O'Neil offers suggestions as to what can be done to "tame" WMDs and reduce their damage (including trading some efficiency for fairness), or better, use the same techniques made available thanks to Big Data for socially productive purposes.
Review of 'Weapons of Math Destruction' on 'Goodreads'
5 stars
One misconception you might have right now is the objective nature of computer algorithms. Cathy O’Neil’s Weapons of Math Destruction first shows you the pinnacle of algorithm objectivity: baseball. Baseball math and algorithms are transparent, measure the event, and are responsive to feedback.
Then, O’Neil pans the camera away to the horror of algorithms that are opaque, rely on proxies, and rarely incorporate feedback: education, finance, mortgages, predictive policing, recidivism, and insurance.
Computer algorithms do not turn their inputs into objective facts. Computer algorithms amplify the biases of the programmer and the dataset. We need to develop a societal understanding of the tools that automate our lives or we’ll forever be manipulated by them.
“Late at night, a police officer finds a drunk man crawling around on his hands and knees under a streetlight. The drunk man tells the officer he’s looking for his wallet. When the officer asks if …
One misconception you might have right now is the objective nature of computer algorithms. Cathy O’Neil’s Weapons of Math Destruction first shows you the pinnacle of algorithm objectivity: baseball. Baseball math and algorithms are transparent, measure the event, and are responsive to feedback.
Then, O’Neil pans the camera away to the horror of algorithms that are opaque, rely on proxies, and rarely incorporate feedback: education, finance, mortgages, predictive policing, recidivism, and insurance.
Computer algorithms do not turn their inputs into objective facts. Computer algorithms amplify the biases of the programmer and the dataset. We need to develop a societal understanding of the tools that automate our lives or we’ll forever be manipulated by them.
“Late at night, a police officer finds a drunk man crawling around on his hands and knees under a streetlight. The drunk man tells the officer he’s looking for his wallet. When the officer asks if he’s sure this is where he dropped the wallet, the man replies that he thinks he more likely dropped it across the street. Then why are you looking over here? the befuddled officer asks. Because the light’s better here, explains the drunk man” (Source: these exact words have 88 Google results).
Weapons of Math Destruction: slpl.bibliocommons.com/item/show/1357767116
Subjects
- Big data
- Social aspects
- Human behavior
- Mathematical models
- Algorithms
- Données volumineuses
- Aspect social
- Comportement humain
- Modèles mathématiques
- Algorithmes