Thursday, October 6 • 14:00 - 15:30

Sign up or log in to save this to your schedule and see who's attending!

Florian Saurwein (1), Natascha Just (2), Michael Latzer (2)
1: Austrian Academy of Sciences | University of Klagenfurt, Austria; 2: University of Zurich, Switzerland
Algorithms on the Internet increasingly shape our daily lives and keep us from drowning in an information flood. The benefits of algorithms are accompanied by risks and governance challenges. This paper contributes to the analysis of governance patterns in the area of algorithmic selection. It explores risks of algorithmic selection, available governance options and the existing governance gaps.

Open algorithmic systems: lessons on opening the black box from Wikipedia
R.Stuart Geiger (1), Aaron Halfaker (2)
1: University of California, Berkeley; 2: Wikimedia Foundation
This paper reports from a multi-year ethnographic study of automated software agents in Wikipedia, where bots play key roles in moderation and gatekeeping. Automated software agents are playing increasingly important roles in how networked publics are governed and gatekept, with internet researchers increasingly focusing on the politics of algorithms. Wikipedia’s bots stand in stark contrast to the algorithmic systems that have been delegated moderation or managerial work in other platforms. In most platforms, algorithmic systems are developed in-house, where there are few measures for public accountability or auditing, much less the ability for publics to shape the design or operation of such systems. However, Wikipedia’s model presents a compelling alternative, where members of the editing community heavily participate in the design and development of such algorithmic systems.

The Algorithmic Politics of Transactional Data
Rachel Anne O'Dwyer
CONNECT, Trinity College Dublin, Ireland
Many powerful stakeholders in the communications space have expanded their interests towards money. This is particularly the case with mobile networks, where financial services are growing in significance. In developing markets mobile money services are already well elaborated, provisioning microfinance, loans, payments and remittances often in the absence of traditional financial actors such as banks. These include airtime trading and mobile money services like M-Pesa, M-Shwari, Smart Money and Branch. What are the implications of this shift for the culture, political economy and governance of future monetary systems, when agencies that control the flow of information now also control the flow of value?
While providing a broad overview of the mobile payments space, this paper will focus on one core aspect of mobile money in developing markets: the aggregation of transactional data by mobile network operators and its use in the provision of new forms of credit scoring and financial offerings. The paper is a case study of a platform that uses transactional data from smart phone use to develop consumer credit scores, Branch. Using interviews and a reverse-engineering method for the study of Branch’s credit scoring algorithm, this paper details the different types of previously latent metadata gathered by Branch from smart phone usage and describes how these are used to tailor new species of credit scoring and financial offerings for the Underbanked. The paper then explores how Branch’s machine-learning algorithms act as a new form of algorithmic governance over money that produces new credit castes and financial instabilities.

The structuration of the filter bubble: A longitudinal field experiment
Cedric Courtois, Lennert Coenen
KU Leuven, Belgium
This paper focuses on the impact of personalized search results on beliefs, attitudes, and behavioural intentions in the fields of ecology, politics, economics, and health care. By means of a longitudinal field study, combining digital methods with questionnaires and lab tests, it assess the interaction between users and search algorithms.

My Algorithm: User Perceptions of Algorithmic Recommendations in Cultural Contexts
Terje Colbjørnsen
University of Oslo, Norway
Automated functions for discovery and recommendations are important features of all the major players in digital media and culture. This paper sets out to explore algorithms in cultural contexts via the users of these services, addressing the following research question: How do users relate to online automated discovery and recommendation services for cultural products and services?
Departing from relevant literature and analyses of Twitter streams, the paper explores how users respond and relate to the automation of cultural discovery and recommendation with specific attention to perceived quality and relevance, along with evaluations of pressures on individual privacy. Preliminary findings indicate how active users seem to take a personal interest in the recommendation algorithms, seeing them as part of their online identities. Accordingly, we find users referring to an automated system for targeted recommendations as “my algorithm”.


Tarleton Gillespie

Microsoft Research


Lennert Coenen

KU Leuven, Belgium

Terje Colbjørnsen

University of Oslo, Norway

Cedric Courtois

KU Leuven, Belgium

Aaron Halfaker

Wikimedia Foundation

Natascha Just

U of Zurich, Switzerland

Michael Latzer

U of Zurich, Switzerland

Rachel Anne O'Dwyer

CONNECT, Trinity College Dublin, Ireland

Florian Saurwein

Austrian Academy of Sciences | University of Klagenfurt, Austria

Thursday October 6, 2016 14:00 - 15:30
HU 1.405 Humboldt University of Berlin Dorotheenstr. 24

Attendees (23)