[Asis-l] Digital Methods Summer School 2018 -- Call for Participation

[Fernando van der Vlist]

Dear all,

The Digital Methods Initiative (DMI) will host its 12th annual Digital
Methods Summer School from July 2-13, 2018 at the University of
Amsterdam, the Netherlands. Below please find the call for
participation.

This year’s theme is: "Retraining the machine: Addressing algorithmic
bias". The deadline for application is May 4, 2018. More information
is available at bit.ly/dmi18-ss-call or email to
summerschool at digitalmethods.net
.

Best regards,

Fernando van der Vlist
Research Associate, Collaborative Research Centre "Media of Cooperation",
University of Siegen
Research Associate, Digital Methods Initiative, University of Amsterdam
Lecturer, New Media and Digital Culture, University of Amsterdam

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# CALL FOR PARTICIPATION
# DIGITAL METHODS SUMMER SCHOOL 2018
# JULY 2-13, 2018
# UNIVERSITY OF AMSTERDAM

# RETRAINING THE MACHINE
# ADDRESSING ALGORITHMIC BIAS

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## DIGITAL METHODS SUMMER SCHOOL

This year's Digital Methods Summer School is dedicated to approaches
to studying so-called machine bias. Discussions have been focusing on
how to hold algorithms accountable for discrimination in their
outputting of results such as in the notorious cases of query results
for 'professional hair' (white women's hair-do's) and 'unprofessional
hair' (black women's' hair-do's). Recently, it was found that search
engine image results for 'pregnancy' and 'unwanted pregnancy' are
similarly divided, with the pregnancy queries returning white skinned
women (mainly bellies, privileging the baby over the woman). 'Unwanted
pregnancy' results in diverse ethnicities. These are new variations on
classic, and still urgent, search engine critiques (once known as
'googlearchies') which questioned the hierarchies built into rankings,
asking who is being authorised by the engine to provide the
information. That work moves forward at the Summer School, building on
examinations of the volatility of engine results, as in the Issue
Dramaturg project, which put on display the drama of websites rising
and falling in their rankings after algorithmic updates, meant to
fight spam, but having unintended, epistemological consequences. More
recently, Facebook newsfeeds have been the source of critique for
their privileging and burying mechanisms, however much they -- like
the engine returns preceding them -- are not easily captured and
documented. Saving engine results has been against the terms of
service; making derivative works out of engine results also breaks the
user contract. Saving, or recording, social media (newsfeed) rolls
seems even less practicable given how feeds are even more
personalised, presumably resisting generalisable findings. User
surveys pointing out unexpected newsfeed results have led to calls for
'algorithmic auditing', a precursor to machine bias critique. As
reported in the technical press, querying social media ad interfaces
shows highly segmented audiences (including racist ones such as
publics to target for 'jew haters' among other available keyword
audiences for sale).

These ad interface results could be repurposed to show which
population segments (as defined by the platforms) are driving the
content choices reflected in the results served. How large are these
discriminatory segments? Capturing, auditing, or repurposing results
are diagnostic practices, identifying under which circumstances
machines could or ought to be retrained. The larger question, however,
concerns how to retrain the machine. One approach lies in query design
-- fashioning queries so as to 're-bias' the results. Others concern
corpus development. For example in stock photography efforts have been
to reimagine ('re-image') women (in the well-known case of Getty
Images' 'Lean In Collection'), however much the images are often used
out of context, as has been found. Yet another one concerns training
and maturing research accounts to trigger controlled algorithmic
responses.

The Digital Methods Summer School is interested in contributing not
only to interpretations of celebrated cases of algorithmic or machine
bias, but also providing diagnostic, query-related, research account
and corpus-building research practices that seek to address the matter
more conceptually.

Expanding the case study collection is also of interest; age
discrimination in Facebook ad interfaces (an American theme) is a
recent example of a telling case study of in-built rather than organic
machine bias, but the international landscape may contribute more to
bias detection, as is the aim of the Summer School. In Twitter there
are feminist bots striving to keep the #metoo space serious, since the
spam has arrived. Which other practices of remaining on topic may be
found, and how may their success and and complications be
characterised? There is also the question of the ramifications of
conceptual contributions to re-biasing for big data science. Which
practical contributions could be made to big data critique?

## APPLICATIONS: KEY DATES

To apply for the Digital Methods Summer School 2018, please use the
University of Amsterdam Summer School form. If that form is not
working, please send (i) a one-page letter explaining how digital
methods training would benefit your current work, (ii) enclose a CV
(with full postal address), (iii) a copy of your passport (details
page only), (iv) a headshot photo, and (v) a 100-word bio (to be
included in the Summer School welcome package). Mark your application
'DMI Training Certificate Program,' and send to
summerschool at digitalmethods.net.

* 4 May: Deadline for applications.
* 7 May: Notifications. Accepted participants will later receive a
welcome package in mid June, which includes a reader, a day-to-day
schedule, and a face book of all participants.
* 18 June: Deadline for summer school fee payments. Participants must
send a proof of payment by this date.

The cost of the Summer School is EUR 895 and is open to PhD candidates
and motivated scholars as well as to research master's students and
advanced master's students. Data journalists, artists, and research
professionals are also welcome to apply. Accepted applicants will be
informed of the bank transfer details upon notice of acceptance to the
Summer School on 7 May. Note: University of Amsterdam students are
exempt from tuition and should state on the application form (under
tuition fee remarks) that they wish to apply for a fee waiver. Please
also provide your student number.

Any questions may be addressed to the Summer School coordinators,
Esther Weltevrede and Fernando van der Vlist:
summerschool at digitalmethods.net. Informal queries may be sent to this
email address as well.