Data science and machine learning books
Did you come here expecting to find the “Hundred-page machine learning book” or “Elements of statistical learning”?
This article is going to be a bit different.
In this article, I want to highlight the 5 books that expose the controversial policies and business models, as well as the surveillance abuses of companies that use artificial intelligence and people’s data at the core of their products.
These are, in my opinion, the “best data science books”:
- Don’t be evil, by Rana Foroohar
- Weapons of math destruction, by Cathy O’Neil
- The age of surveillance capitalism, by Shoshana Zuboff
- Algorithms of oppression, by Safiya Umoja Noble
- Stolen focus, by Johann Hari
Let’s dive in.
This article was originally published on Train in Data’s blog.
Don’t be evil
Don’t be Evil investigates how today’s most powerful corporations are upending our economies, tainting our political systems, and clouding our minds with their dubious practices, abuses of surveillance, and market share supremacy.
This book examines the business strategies used by the FAANGS — Facebook, Apple, Amazon, Netflix, and Google — five major technology companies and how they came to rule their respective markets.
It explores their exploitative practices, including:
- digital surveillance and lack of privacy.
- spreading of misinformation and hate speech.
- predatory algorithms targeting the weak and vulnerable.
- products engineered to manipulate our desires.
Weapons of math destruction
In “Weapons of Math Destruction,” the use of machine learning in practical applications to perpetuate existing inequity is examined.
The author looks at how biases in machine learning models used in a variety of industries, including insurance, advertising, education, and law enforcement, can result in choices that hurt the underprivileged, strengthen discrimination, and amplify inequality.
The author argues that we should exercise caution while creating prediction models and selecting the datasets we employ to train them.
Algorithms are, after all, “opinions embedded in code,” and they may be biased, as with any opinions.
The age of surveillance capitalism
Surveillance capitalism is a new economic order that claims human experience as free raw material for hidden commercial extraction, prediction, and sales practices.
Businesses assert that they can use our private human experience as a free source of raw material. They processed our experience through artificial intelligence factories that crate products able to anticipate our behavior. They then sell these products to other businesses for a profit.
“The Age of Surveillance Capitalism” offers an in-depth explanation of the new economic system that has come to rule our communities, covering the principles of surveillance capitalism, how it functions, how it is spreading, and how it affects both our lives and our democracy.
Algorithms of oppression
People believe that the results they receive from online searches — the majority of which are on Google — are reliable, honest, impartial, and objective. However, the items that show up at the top of the search have a lot to do with the money made through advertising.
In “Algorithm of Oppression,” it is explained how a biased set of search algorithms that favor whiteness and discriminate against people of color are the result of private interests and the monopoly status of a relatively limited number of internet search engines.
And since we are talking about search engines, did you consider trying Google alternatives like Ecosia and DuckDuckGo?
Stolen Focus
Apps like Instagram and other social media platforms, are designed to get you to use them for as long as possible, and as often as possible. The more time you spend on the app, the more money they make.
In Stolen Focus, we learn how social media interferes with our individual and collective ability to focus and the consequences it has on our lives and societies.
If you enjoy the content in these books, you may also find these data science movies interesting.
Check out the original article at Train in Data blog for more details.
