[Air-L] cases in which data-driven decision-making went awry

[Bridges, Lauren]

Highly recommend "Automating Inequality" by Virginia Eubanks.

https://us.macmillan.com/books/9781250074317

Cheers!

On 5/14/18, 6:10 PM, "Air-L on behalf of Stefaan Verhulst" <air-l-bounces at listserv.aoir.org on behalf of SVerhulst at markle.org> wrote:

    Perhaps of interest: I make reference to a few “bad data cases” in my short presentation on "why we should care about bad data” available at : http://thegovlab.org/why-we-should-care-about-bad-data/
    There are also a few “challenging data case studies” at http://odimpact.org/ e.g. http://odimpact.org/case-united-states-eightmaps.html
    
    On May 14, 2018, at 5:28 PM, Sheryl Grant <sherylgrant at gmail.com<mailto:sherylgrant at gmail.com>> wrote:
    
    I apologize in advance that this is an imperfectly phrased query.
    
    In short, I'm looking for literature about terrible data governance and
    related issues. Basically, what happens when there are errors in automated
    data systems, how those errors might have occurred, and what institutions
    do (or don't) when they discover those errors. Ideally, cases would
    describe the technical bits as well as the human choices made.
    
    Another way to say it is that my colleagues and I are looking for
    investigations into data-driven decision-making gone awry.
    
    I've read Kathy O'Neill's Weapons of Math Destruction, which was excellent,
    and now I'm looking for more specific cases, if they exist.
    
    Thanks,
    
    Sheryl Grant
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