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Comments (17)

Weapons of Math Destruction

How Big Data Increases Inequality and Threatens Democracy
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Jul 21, 2023aileeen rated this title 5 out of 5 stars
Very engaging read about the impact of discriminatory algorithms—I enjoyed the various examples the author discussed, including some examples of how data science has been used to positively impact people's lives. This book is a must-read…
Nov 17, 2021Hopalong_Kid rated this title 4.5 out of 5 stars
Important, well written assessment of the influence of corrupting bias of data and algorithms.
Sep 06, 2021Boych2018 rated this title 5 out of 5 stars
Author tends to be verbose in places but she does a good job of presenting her concerns.
Jul 15, 2021isabelc_206 rated this title 5 out of 5 stars
Truly a must read! The book doesn't get too technical in terms of the back-end computer science/math-y side of algorithms, leading it to be a great read for *anyone* interested in the social impacts of algorithms. O'Neil wonderfully…
Jul 06, 2021FCLSKirkwood rated this title 5 out of 5 stars
This is a fascinating read. It breaks down systemic discrimination in easy-to-understand language. I agree with our comments, this should be required reading for students and perhaps some COO's, CIO's CFO's, etc.
Aug 29, 2020dirtbag1 rated this title 5 out of 5 stars
Should be required reading for all high school students. Too important to ignore.
Nov 08, 2018JonMoss rated this title 2.5 out of 5 stars
Read for Strangr Than Fiction book discussion group. Join us for the discussion at the Plaza Branch on Tuesday 11/13/2018
Oct 17, 2018
A very good read, although I do wish she had listed more studies to support her findings. Definitely one more thing that big data is doing to undermine the little guy
Sep 22, 2018gaetanlion rated this title 1 out of 5 stars
Unbalanced. Although I did find the book interesting, the author pretty much rants for 300 pages. Her theories are supported by isolated anecdotes. She invariably points out whenever WMD models have miserably failed and caused much…
Sep 12, 2018
Big Data, Algorithms, and biased stats. If you're a minority or part of the disenfranchised this book reveals what we've known for decades but intensified with web 2.0. Data mining and with Facebook being under fire as well as Amazon for…
Dec 16, 2017StarGladiator rated this title 2 out of 5 stars
Updated review: There are some pretty good books on the nefarious algorithms out there and their corporate owners, but this is one of the more mediocre ones. I recently heard an annoying talk by this author in Seattle. Some of the…
Jul 31, 2017ScienceMommy rated this title 5 out of 5 stars
OMG! . The author is cogent, extremely engaging and writes in a way that draws you in and makes continuing on effortless...as she educates you about what is going on and gives real life examples of how critical things get delegated to…
Jul 23, 2017
This is an interesting read for someone in his/her seventh decade (i.e. me) and a must read for someone in their third, fourth and fifth decade. It’s always important to understand what you are up against. (There is likely an algorithm…
May 26, 2017JCLChrisK rated this title 4 out of 5 stars
You are prey. The predator is numbers. Numbers that have been carefully designed to turn you into prey. Numbers wielded by marketers, politicians, insurance companies, and so many others. The problem with these particular numbers is that…
Feb 09, 2017
In the current political and technological moment, I can't imagine a more important book than this. The author - mathematician, investment software technician, and social critic - opened my eyes to the profound and multiple ways that…
Dec 01, 2016GummiGirl rated this title 4 out of 5 stars
A useful compendium of various common algorithms and their limitations. Although many of these are already well known, the author ties them together effectively with her central thesis: they are all contributing to inequality.
Nov 15, 2016
A thorough, timely, and accessible explanation of the ways in which supposedly 'neutral' mathematical algorithms and models can very easily be misused in ways that further entrench systemic inequalities and biases.