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 https://bit.ly/dmi18-ss-call or email to summerschool@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 -- # CALL FOR PARTICIPATION # DIGITAL METHODS SUMMER SCHOOL 2018 # JULY 2-13, 2018 # UNIVERSITY OF AMSTERDAM # RETRAINING THE MACHINE # ADDRESSING ALGORITHMIC BIAS -- ## 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@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@digitalmethods.net. Informal queries may be sent to this email address as well.