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Ancsa Hannak and I wanted to point you to an exciting workshop at ICWSM (June 11th, Munich) called Complex Systems Perspectives on Algorithmic Bias

We encourage you to submit your 1-2 page papers on a range of topics relating to bias, but obviously most relevant here, for any simulation models of bias in online systems.  Papers can include position papers, initial results, summaries of published papers and the authors of accepted papers will be given an 8-10 minute slot to present their work and receive feedback during the workshop.

Please submit all inquiries about the CfP and any submissions to [log in to unmask]

Submission deadline: April 7th
Decisions sent: April 12th
Workshop date: June 11th

We have an exciting lineup of speakers and panelist, including keynotes from Krishna Gummadi and Seda Gurses, and panelists from law, sociology, computer science and psychology.  We look forward to similarly diverse submissions! 

Aniko Hannak & Kenny Joseph

More info on the workshop below:

From search result ranking to friend recommendation, algorithms used in today’s online platforms exist within a coevolving universe of model parameters, institutional constraints and the whims of platform users. This workshop will focus on the development and exposition of research that seeks to model, quantify or theorize this complex system of algorithms, platforms and users. Through a mix of presentations of accepted abstracts, presentations from keynote speakers, and panel discussions, this workshop will bring to the forefront empirical, theoretical, and simulation-based research that helps to clarify this interplay using a complex systems perspective. We take a broad view of what a “complex system perspective” entails, emphasizing only that there is a goal of understanding how these various pieces fit and evolve together. As such, we are interested in work on, e.g., algorithmic feedback loops and the impact of algorithmic changes on social media systems, using a variety of methodological approaches, e.g. qualitative interviews, empirical studies, and agent-based models.

Themes of the workshop:

1. How can we leverage quantitative, qualitative, and simulation-based methods to push forward our understanding of algorithmic bias from a complex systems perspective?

Within our community, research is dominated by quantitative study of observational data. Such an approach has provided important insights into algorithmic bias, but also cannot easily be used to study feedback loops that help produce this bias. This workshop will discuss new approaches to quantitative evaluation of observational data, as well as new quantitative experimental approaches, but also will emphasize the benefits of qualitative and simulation-based approaches. Qualitative study can help to identify, e.g., how algorithms are (or are not) put to work across difficult-to-quantify social contexts2. Similarly, while identifying problems in real systems through empirical measurements is a great first step towards accountability, we can not possibly investigate each system one-by-one, as they change over time, etc. using empirical measurements. To study these kinds of behaviors, it is instead useful to approach such a problem from a simulation perspective. Using simulation, we can assess how different potential changes to the system impact the evolution of platforms and their users over time.

2. How can theory from the social sciences (and/or social physics) help us to understand complex systems of algorithmic bias?

In addition to the various methodological perspectives that must be brought to bear on this problem, this workshop also emphasizes the need for various theoretical perspectives. In many cases, applicable theory from the social sciences may not be known to, e.g., computer scientists, and conceptual tools from social physics used to describe complex systems may be unknown to both social scientists and computer scientists. To this end, this workshop seeks to bring to bear theoretical perspectives from across these disciplines that use a complex systems perspective to help explain or understand algorithmic bias.

3. Can (and should) we try to “fix” algorithmic bias? If so, what does that solution look like? If not, why, and what should we be doing instead?

Recent research has argued that the problem of algorithmic bias lies largely in structures of bias and discrimination that lay beyond the boundaries of model parameters. Further, developing a “fair” algorithm may be difficult, particularly because definitions of fairness are themselves varying and socially constructed. At the same time, however, at the scale of today’s social media, it is obvious that some automation is required if we wish to, e.g. provide friend recommendations on Facebook. How, then, can we move forward in a world where algorithms seem destined to be both necessary and that may never fully be "de-biased”?
Kenny Joseph
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