I'm a data person. I pride myself on being logical and looking at the numbers before making decisions. And for quite a few years, I worked at a data visualization company and was a self-professed data geek. But can more data actually lead to worse results? That is what Weapons of Math Destruction tries to understand.
Insightful and timely, this book provides a detailed look at how algorithms based on big data don't always tell the truth or lead to a more fair world as they are purported to do. Rather, they contribute to a system that is opaque and hard to challenge, increasing the divide between the privileged and everyone else.
Each chapter provides a thoughtful exploration of an area where big data is supposed to be helping, such as college rankings, recidivism of convicts, applying for jobs, and getting loans. Algorithms in each area help define which are the best colleges, which convicts are most likely to reoffend, what personality types are best suited for a job, and who should get the best interest rates. That sounds useful, right?
But unfortunately, ideal data is not always available, so bad or irrelevant data is often used instead. And the resulting predictions are treated as gospel, increasing efficiency of the system, but harming those caught on the wrong side. It hurts a segment of the population while providing the rest of us with the false belief that fairness and justice has being done. In many cases, the algorithms' predictions create a negative feedback loop, directly influencing the outcome they were objectively trying to determine.
I found this book to be interesting and relevant. It really goes to show that your predictions are only as good as the data you've got. Whether you're a data geek like me or you just want to learn a little more about big data's potentially harmful effects, this is a worthwhile book to check out.
Readaroo Rating: 4 stars
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