Weapons of Math Destruction

From P2P Foundation
Revision as of 06:33, 30 October 2016 by Mbauwens (talk | contribs) (Created page with " '''* Book: Cathy O’Neil. Weapons of Math Destruction: big data increases inequality and threatens democracy.''' URL = https://weaponsofmathdestructionbook.com/ =Review=...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

* Book: Cathy O’Neil. Weapons of Math Destruction: big data increases inequality and threatens democracy.

URL = https://weaponsofmathdestructionbook.com/


Review

Evelyn Lamb:

"Weapons of math destruction, which O’Neil refers to throughout the book as WMDs, are mathematical models or algorithms that claim to quantify important traits: teacher quality, recidivism risk, creditworthiness but have harmful outcomes and often reinforce inequality, keeping the poor poor and the rich rich. They have three things in common: opacity, scale, and damage. They are often proprietary or otherwise shielded from prying eyes, so they have the effect of being a black box. They affect large numbers of people, increasing the chances that they get it wrong for some of them. And they have a negative effect on people, perhaps by encoding racism or other biases into an algorithm or enabling predatory companies to advertise selectively to vulnerable people, or even by causing a global financial crisis. O’Neil is an ideal person to write this book. She is an academic mathematician turned Wall Street quant turned data scientist who has been involved in Occupy Wall Street and recently started an algorithmic auditing company. She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our lives and against the notion that an algorithm, because it is implemented by an unemotional machine, cannot perpetrate bias or injustice.

...

O’Neil talks about financial WMDs and her experiences , but the examples in her book come from many other facets of life as well: college rankings, employment application screeners, policing and sentencing algorithms, workplace wellness programs, and the many inappropriate ways credit scores reward the rich and punish the poor. As an example of the latter, she shares the galling statistic that “in Florida, adults with clean driving records and poor credit scores paid an average of $1552 more than the same drivers with excellent credit and a drunk driving conviction.” (Emphasis hers.)

She shares stories of people who have been deemed unworthy in some way by an algorithm. There’s the highly-regarded teacher who is fired due to a low score on a teacher assessment tool, the college student who couldn’t get a minimum wage job at a grocery store due to his answers on a personality test, the people whose credit card spending limits were lowered because they shopped at certain stores. To add insult to injury, the algorithms that judged them are completely opaque and unassailable. People often have no recourse when the algorithm makes a mistake.

Many WMDs create feedback loops that perpetuate injustice. Recidivism models and predictive policing algorithms—programs that send officers to patrol certain locations based on crime data—are rife with the potential for harmful feedback loops." (https://blogs.scientificamerican.com/roots-of-unity/review-weapons-of-math-destruction/)


More Information