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

[Phoebe Moore]

I have written the section on risks of psychosocial and physical violence
in digitalized workplaces for the International Labour Organization (of the
UN) discussions at the forthcoming International Labour Conference in
Geneva end of this month toward a new labour convention.

One of the points I make (frequently) is that decisionmaking based on
algorithm in the workplace brings up extensive ethical questions and can
even lead to risky environments for workers. This includes e.g. AI in
people analytics for recruitment, appraisal, talent spotting and use of
algorithms in other human resource decisions in gig work.

I can send the final draft if you are interested. An earlier draft is
linked here at the launch of the report
 http://www.ilo.org/actrav/events/WCMS_616826/lang--en/index.htm


Dr Phoebe V Moore
Personal email pvm.doc at gmail.com
Work email pm358 at le.ac.uk <p.moore at mdx.ac.uk>
Twitter @phoebemoore
Biolog http://phoebevmoore.wordpress.com/


On 15 May 2018 at 19:49, Sheryl Grant <sherylgrant at gmail.com> wrote:

> Thanks for the replies, all. I'm working my way through them and having a
> heyday.
>
> This is what I am trying to drill into: The accuracy of big data
> applications will be affected by the accuracy of small data.
>
> And all the things that can go wrong in the data lifecycle that thwart
> accuracy.
>
> Many thanks for all your suggestions so I could articulate this better.
>
> Sheryl
>
> On Mon, May 14, 2018 at 7:10 PM, Paul Henman <p.henman at uq.edu.au> wrote:
>
> > Re RoboDebt
> >
> > See my conference paper Henman, Paul. (2017, September 4). The computer
> > says 'DEBT': Towards a critical sociology of algorithms and algorithmic
> > governance. Data for Policy Conference 2017. Zenodo.
> 10.5281/zenodo.884116
> > at https://zenodo.org/record/884117#.WcTlEsh97IU
> >
> > I'm happy to discuss technical bits further if required.
> >
> > Paul Henman
> > Associate Professor of Digital Sociology and Social Policy
> > Director, Bachelor of Social Science
> > School of Social Science
> > University of Queensland  QLD  4072
> > T: +61 7 3365 2765 | E: P.Henman at uq.edu.au | W:
> > www.digitalsocialpolicy.com
> >
> > UQ ALLY  - Supporting the diversity of sexuality and gender identity at
> UQ.
> > CRICOS Provider Number: 00025B
> > -----Original Message-----
> > From: Air-L <air-l-bounces at listserv.aoir.org> On Behalf Of Deborah
> Lupton
> > Sent: Tuesday, 15 May 2018 8:18 AM
> > To: Sheryl Grant <sherylgrant at gmail.com>
> > Cc: <air-l at listserv.aoir.org> <air-l at listserv.aoir.org>
> > Subject: Re: [Air-L] cases in which data-driven decision-making went awry
> >
> > The Australian Government's social services stuff-up 'robo-debt' is a
> good
> > example:
> >
> > https://www.smh.com.au/politics/federal/robo-debt-an-
> > unlawful-exercise-former-appeals-tribunal-member-says-
> 20180405-p4z7x9.html
> >
> >
> >
> > On Tue, May 15, 2018 at 7:28 AM, 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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