Historian of antebellum technology and contemporary diplomacy.
Mastodon: historians.social/@awmarrs
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60% complete! Aaron has read 24 of 40 books.
When sixteen-year-old Rashad is mistakenly accused of stealing, classmate Quinn witnesses his brutal beating at the hands of a police …
When sixteen-year-old Rashad is mistakenly accused of stealing, classmate Quinn witnesses his brutal beating at the hands of a police …
Classic work in the field -- an excellent overview of the topic.
Good, readable, and brief overview of the history of abolition in Latin America, with particular attention paid to the linkages throughout the region (to the American and Haitian Revolutions, links among the countries in South America, activities in Europe which pushed abolition forward or held it back).
One might think that every aspect of Lincoln's political life would be a well-plowed field at this point, but May manages to find some new ground. The result is a view of an antebellum election that we don't often see: one that centers the role that foreign policy played in campaigning and electoral arguments. Of course, at this time even foreign policy was linked to the domestic issue of slavery, and May brings his considerable experience in the history of foreign policy to bear on this significant issue.
Blanchard tells the story of enslaved people who fought for freedom during Latin America's wars for independence, and charts what happened to them after those independence movements were successful. The book takes a wide view of multiple countries and helps us understand why the involvement of enslaved people in the fight for freedom did not automatically lead to their own freedom.
In this book, Schellmann takes a deep dive into the artificial intelligence tools that employers use to vet, monitor, and fire their employees. She surveys the landscape, and, frankly, it is terrifying and depressing. What seems particularly mind-numbing is that these AI tools don't seem to be very effective, but employers desperate to cut costs (regardless of the cost to the humanity of their employees) keep adopting them anyway. The allure of a "scientific" solution is so powerful that employers ignore or don't care about the downsides. Schellmann draws a comparison to phrenology at one point that is both apt and bleak. Unfortunately, the book could have used another round of editing, and gets repetitive in places (including almost the exact same quotation appearing on pages 56 and 73). But if you are on the job market or know someone who is, you might as well pick this up and …
In this book, Schellmann takes a deep dive into the artificial intelligence tools that employers use to vet, monitor, and fire their employees. She surveys the landscape, and, frankly, it is terrifying and depressing. What seems particularly mind-numbing is that these AI tools don't seem to be very effective, but employers desperate to cut costs (regardless of the cost to the humanity of their employees) keep adopting them anyway. The allure of a "scientific" solution is so powerful that employers ignore or don't care about the downsides. Schellmann draws a comparison to phrenology at one point that is both apt and bleak. Unfortunately, the book could have used another round of editing, and gets repetitive in places (including almost the exact same quotation appearing on pages 56 and 73). But if you are on the job market or know someone who is, you might as well pick this up and read it to learn more about the utter contempt that today's employers have for their employees through their continued use of these unproven and ethically-questionable-at-best tools.
David Futrell was the director of selection and assessment at Walmart, which is the largest private employer in the United States. He and his team ran one of the largest hiring machines on the planet and assessed thousands of Walmart applicants every day. He shared his experience wit AI and machine learning tools on a panel at an industry conference in 2020: "I was very excited by these tools whenever I first started seeing them. So I've been doing a kind of traditional prediction using regression and correlation techniques for many years and a lot of these machine learning tools are just super fast and sophisticated ways to test all of the possible combinations." Because Futrell's team assessed thousands of people a day, he had an abundance of data at his fingertips and didn't run into such problems as small sample size, which could skew results. He asked his team to test a lot of the new AI tools. And he was disappointed: "When we tested this and in practice, we didn't find that machine learning predictions to be substantially better than the old-fashioned way." So, neither in computer labs nor in applied settings did AI outperform traditional statistical models. And neither method achieved much statistical relevance.
— Algorithm by Hilke Schellmann (Page 279 - 280)
A stunning achievement. Kelley has written a detailed and persuasive assessment of the role that the United States played in the international slave trade. He argues' that the importance of the United States in this trade shifted over time; even after the supposed end of the United States' involvement in the trade after 1808, U.S. businessmen continued to profit from trade in human beings through their connections, by supplying or building ships which were part of the trade, and so on. Lucidly written and compellingly argued.
When technology reinforces inequality, it's not just a glitch—it's a signal that we need to …
This book is a comprehensive overview of issues of bias in technology. Broussard tackles a range of topics and does so systematically. Her examples are timely and well-chosen, and leave little doubt as to the deep problems that we face with technology, and the utter indifference of most people working in this space. If we are to truly effect change, we first have to recognize the problems and think about how to solve them; this book is an important first step in that process.
When technology reinforces inequality, it's not just a glitch—it's a signal that we need to redesign our systems to create …
When technology reinforces inequality, it's not just a glitch—it's a signal that we need to …
Tech is racist and sexist and ableist because the world is so. Computers just reflect the existing reality and suggest that things will stay the same—they predict the status quo. By adopting a more critical view of technology, and by being choosier about the tech we allow into our lives and our society, we can employ technology to stop reproducing the world as it is, and get us closer to a world that is truly more just.
— More Than a Glitch by Meredith Broussard (Page 188)
When technology reinforces inequality, it's not just a glitch—it's a signal that we need to …
The bureaucrats who decided to use a computer to assign grades are guilty of technochauvinism. They thought that the algorithm would offer objective, fair grades and that the computational solution would be sufficient in a time of global crisis. It wasn't, because a grade in education is a social decision, not merely a mathematical one. Computers are rarely the fairest solution when it comes to social decisions.
— More Than a Glitch by Meredith Broussard (Page 69)