A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. . . It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
Longlisted for the National Book Award | New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric. We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. . .This important book empowers us to ask the tough questions, uncover the truth, and demand change.
A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally-hiring, driving, paying bills, even choosing romantic partners-that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology.
Jer Thorp's analysis of the word "data" in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with "data," we find not only its classic companions "information" and "digital," but also a variety of new neighbors--from "scandal" and "misinformation" to "ethics," "friends," and "play." To live in data in the twenty-first century is to be incessantly extracted from, classified and categorized, statisti-fied, sold, and surveilled. Data--our data--is mined and processed for profit, power, and political gain. In Living in Data, Thorp asks a crucial question of our time: How do we stop passively inhabiting data, and instead become active citizens of it?
Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.
Mathias Spielkamp, "Inspecting Algorithms for Bias," MIT Technology Review, June 12, 2017. "Gummadi, who has extensively researched fairness in algorithms, says ProPublica’s and Northpointe’s results don’t contradict each other. They differ because they use different measures of fairness."