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Bill Fulkerson

The eruption that helped to destroy one of China's great dynasties : Research Highlights - 0 views

www.nature.com/...d41586-020-02467-2

eruption china

shared by Bill Fulkerson on 10 Sep 20 - No Cached
  • Bill Fulkerson on 10 Sep 20
     
    The collapse of China's prosperous Ming dynasty, one of the most stable in Chinese history, has been attributed, in part, to the 1641 eruption of a volcano thousands of kilometres from the imperial capital in Beijing.
Bill Fulkerson

Coronavirus antigen tests: quick and cheap, but too often wrong? | Science | AAAS - 0 views

www.sciencemag.org/...uick-and-cheap-too-often-wrong

quick tests antigen wrong

shared by Bill Fulkerson on 26 May 20 - No Cached
  • Bill Fulkerson on 26 May 20
     
    Antigen tests don't amplify their protein signal, so they are inherently less sensitive. To make matters worse, that signal gets diluted when samples are mixed with the liquid needed to enable the material to flow across test strips. As a result, most antigen tests have a sensitivity of anywhere between 50% and 90%-in other words, one in two infected people might incorrectly be told they don't have the virus. Last month, Spanish health authorities returned thousands of SARS-CoV-2 antigen tests to the Chinese firm Shengzhen Bioeasy Biotechnology after finding the tests correctly identified infected people only 30% of the time, according to a report by the Spanish newspaper El Pais.
Steve Bosserman

Chinese researcher claims first gene-edited babies - 0 views

www.apnews.com/97bb7aa36c45449b488e19ac83e86d

gene-editing crispr ethics decision-making

shared by Steve Bosserman on 26 Nov 18 - No Cached
  • “I feel a strong responsibility that it’s not just to make a first, but also make it an example,” He told the AP. “Society will decide what to do next” in terms of allowing or forbidding such science.
Steve Bosserman

Are You Creditworthy? The Algorithm Will Decide. - 0 views

undark.org/...redit-scoring-machine-learning

algorithms discrimination bias credit scoring

shared by Steve Bosserman on 12 May 18 - No Cached
  • The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.
  • Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.
  • By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.
  • ...1 more annotation...
  • These systems are fast becoming the norm. The Chinese Government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm.
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