Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

254, pagine

Pubblicato il 06 Settembre 2016 da Broadway Books.

ISBN:
978-0-553-41882-8
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4 stelle (5 recensioni)

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 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 urgent and necessary 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 zip code), he’s then cut off from the kind of …

12 edizioni

Weapons of Math Destruction

5 stelle

O'Neil is an excellent guide to the world of algorithms and how they impact our lives in ways large and small. To me one of the most depressing lessons of the book was that the people creating these systems which analyze data have almost no interest in the knock-on effects that their work has on people's lives. They are so focused on whatever particular goal they have that they have total blinders as to the damaging effects that their work might have. As with any book about technology the companies have changed a bit since O'Neil wrote the book, but the underlying problems have not changed; thus this continues to be a worthy guide to the challenges that our society will face as we continue to be enmeshed in algorithms which may not have our best interests at heart.

An excellent demonstration of the devastating pervasiveness of Big Data

4 stelle

This book takes you on a journey through all areas of life and shows how Big Data systems cause harm in all of them. Through the examination of these case studies, it also gets to the fundamental issues with Big Data and proposes ways to change our perspectives on it.

This book is really good. It is clear, understandable for a layperson and very well-rounded. I would give it a 5/5 if there weren't these two points:

  • it is completely US-centric. The case studies are all domestic. This weakens its explaining power for the rest of the world, imo. (this isn't to say that it doesn't make sense or that it's wrong for a US citizen to only write about the US)
  • it's 8 years old now, and while it's analyses are not at all outdated, the world of Big Data has evolved since 2016. I often wondered what ended …

ha recensito Weapons of math destruction di Cathy O'Neil

Suit up for Combat!

5 stelle

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.

Review of 'Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy' on 'Goodreads'

4 stelle

Cathy O'Neil, nota in rete come MathBabe, dopo la laurea in matematica e un inizio di carriera universitaria è passata al settore privato nel campo dell'economia, e si è trovata in mezzo alla crisi del 2008. Il risultato della sua rivisitazione di quello che è successo l'ha portata a scrivere questo libro, che trovate anche in edizione italiana, sulle "armi di distruzione matematica" (ADM). Chi è allergico alle formule non tema, perché qui non ce ne sono per nulla. Quello che viene spiegato in teoria ed esemplificato in vari modi è il concetto di cui al titolo: in poche parole si tratta di modelli matematici complessi, spesso legati ai big data o comunque a dati che non nascono direttamente per quella ragione, che risultano oscuri agli esseri umani e soprattutto sono fatti in modo da non accettare feedback a posteriori. Quest'ultima è la vera caratteristica che secondo O'Neil li …

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