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

[Samantha Close]

I'm not sure if this is what you mean, but there is a great paper on
unexpected and sometimes disturbing behavior from machine-learning
algorithms here: https://arxiv.org/pdf/1803.03453.pdf

and a more popular press write-up of their study here:
http://aiweirdness.com/post/172894792687/when-algorithms-surprise-us

Safiya Noble's book *Algorithms of Oppression* is fantastic on the more
"here is the harm that results from certain kinds of data-driven decision
making" aspect of your question. You might also look at Eden Medina's
book *Cybernetic
Revolutionaries: Technology and Politics in Allende's Chile* on how and why
an early attempt to run a country based on data and algorithmic decision
went caput.

On Mon, May 14, 2018 at 4:28 PM, Sheryl Grant <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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