Matthew Katz
I'm a doctor dedicated to improving health. Fortunate son, husband, father. Asking questions to help patients, health professionals, citizens. Neutral good.
Book Recommendations:
Recommended by Matthew Katz
“@drbeckta @HenningWillers @DrChowdharyMD @OncoAlert @ParikhSimul @ACROresident @SprakerMDPhD @ARRO_org @VUMCRadOnc @IJROBP @QuadShotNews @ASTRO_org Yes, it’s complicated. Here is a great book for those interested in the pre-WW2 history of radiation in the U.S. #radonc #history https://t.co/1ezOYNj9RX https://t.co/9mAIbGxRnS” (from X)
by Matthew Lavine·You?
At the close of the 19th century, strange new forms of energy arrested the American public's attention in ways that no scientific discovery ever had before. This groundbreaking cultural history tells the story of the first nuclear culture, one whose lasting effects would be seen in the familiar "atomic age" of the post-war twentieth century.
Recommended by Matthew Katz
“@techreview Great read along these lines and how #AI may reinforce racism in code: @mathbabedotorg 's book "Weapons of Math Destruction" https://t.co/8Q8W5V7Svl” (from X)
by Cathy O'Neil·You?
NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword “A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.