Review
I dictated this review. Hence it reads like a stream of thought. I fixed up some words it got wrong, but I’m not going to put much more effort in than that. I didn’t love this book. Its point is basically “don’t trust algorithms to solve the world’s problems” - but it didn’t really propose anything better.
Begin Dictation
Weapons of mass destruction was written by a mathematics PhD. It is very confusing to me. The author is clearly very smart, having taught mathematics at University for a while before going to apply her craft in the World of finance, then as a part of a few think thanks. I have a couple of problems with this book, though. On its face, this book simply served as a warning that algorithms, big data, and machine learning are not perfect. In particular she uses the case examples from the legal system and housing market to describe how we have come to a scribe great trust in algorithms - to the detriment of some people who are negatively affected by those algorithms. Some examples include an algorithm that implicitly factored in the race when determining The likelihood of recidivism of a person who is about to be sentenced for a crime, and would routinely turn off work for four black and Latino man from the ghetto, because the ghetto has more crime in it and therefore (the algorithm inferences) they are more likely to commit crime when they get out. The author takes great umbrage with this interpretation, and says it is essentially unconstitutional because it is factoring in race if you said Tuesday. She also said that the Algorithm on the whole app at the debt affect of lowering the average to do, and that it has worked for his intended purpose of being more fair between cases and giving the judge a measuring stick that is common with his judge peers. My problem with this book come From her pointing out a bunch of problems with mathematical models and algorithm and big data and machine learning lesson not providing any better examples or alternative. At least not here in the first part of the book. Maybe the point of this book is just two ways awareness Hope take algorithm can be problematic, and that we should use them as tools like what they are is not of God. But she has not convincingly swayed me into thinking that they are on the whole bad. Maybe that’s not her intended point, but it seems that way from the onset. She does talk about how well the mystical models work in an application like baseball. And she stands up on Wife that might be true. Baseball algorithms are face around A common definition of open and transparent data. They intend to project the exact same data that they are measuring, and are not being based off of property measures, and are therefore 100% representative of the predictions they are intending to create.
Notes
LIST FROM [[#]]