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

Reviewed by paulml

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

Cathy O'Neil, Crown Publishing, 2016

Big data and algorithms are supposed to be the "saviors" of our modern world.  With them, a corporation or a government is supposed to be able to measure and analyze almost anything.  But what if those algorithms are very flawed?

Among the suggestions to fix American education is to get rid of bad teachers.  Standardized test scores are one way to find those bad teachers.  What if the students didn't learn the basics of math, for instance, in a lower grade?  What if the teachers in that lower grade blatantly corrected the tests before submitting them to make them selves look better?  If the test scores for a class are not as good as the algorithm predicted, then that teacher is out the door.  There is no way to fix that algorithm, to bring it more in line with reality.

Crime prediction software sounds like a godsend to cash-strapped police departments.  Why not concentrate resources in areas where there is predicted to be a better chance of crime?  If a police department includes "nuisance" crime, like underage drinking or pot smoking in public, the algorithm will send units to that neighborhood on an increased basis.  If it happens to be a minority neighborhood, and otherwise is law-abiding, the residents can expect more instances of "stop and frisk."  Again, changing that algorithm is not possible.

At work, it is not possible to change the algorithm that makes the employee schedule because a person has transportation or child care issues.  Profit comes first.  "Clopening" is when an employee at Starbucks, for instance, closes the store at 11 p.m., then has to return in a few hours to open at 5 a.m. and work a full shift.

Algorithms have their good and bad points.  The biggest bad point is that there is no way to change them and get them to conform to the real world.  Written by a data scientist, this book is a big eye opener and is very much worth reading.