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Michael Ekstrand <[log in to unmask]>
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Michael Ekstrand <[log in to unmask]>
Mon, 5 Mar 2018 21:26:10 -0700
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                         CALL FOR PAPERS

Workshop on Fairness in User Modeling, Adaptation, and Personalization

At the ACM Conference on User Modeling, Adaptation, and Personalization
(UMAP 2018)
8-11 July, 2018 at Nanyang Technological University, Singapore

Workshop website:
Conference website:

                        WORKSHOP DESCRIPTION

Personalization has become a ubiquitous and essential part of systems that
help users find relevant information in today's highly complex
information-rich online environments. Machine learning, recommender
systems, and user modeling are key enabling technologies that allow
intelligent systems to learn from users and adapt their output to users'
needs and preferences. However, there has been a growing recognition that
these underlying technologies raise novel ethical, legal, and policy
challenges.  It has become apparent that a single-minded focus on the user
preferences has obscured other important and beneficial outcomes such
systems must be able to deliver. System properties such as fairness,
transparency, balance, openness to diversity, and other social welfare
considerations are not captured by typical metrics based on which
data-driven personalized models are optimized. Indeed, widely-used
personalization systems in such popular sites such as Facebook, Google News
and Y!
 ouTube have been heavily criticized for personalizing information delivery
too heavily at the cost of these other objectives.

Bias and fairness in machine learning are topics of considerable recent
research interest. However, more work is needed to expand and extend this
work into algorithmic and modeling approaches where personalization is of
primary importance. The goal of this workshop is to bring together a
growing community of experts from academia and industry to discuss ethical,
social, and legal concerns related to personalization, and specifically to
explore a variety of mechanisms and modeling approaches that help mitigate
bias and achieve fairness in personalized systems.

                        TOPICS OF INTEREST

Topics of interest include, but are not limited to the following.

- Bias and discrimination in user modeling, personalization and
- Computational techniques and algorithms for fairness-aware personalization
- Definitions, metrics and criteria for optimizing and evaluating
fairness-related aspects of personalized systems
- Data preprocessing and transformation methods to address bias in training
- User modeling approaches that take fairness and bias into account
- User studies to evaluate impact of personalization on fairness, balance,
diversity, and other social welfare criteria
- Balancing needs of multiple stakeholders in recommender systems and other
personalized systems
- "Filter bubble" or "balkanization" effects of personalization
- Transparent and accurate explanations for recommendations and other
personalization outcomes

                         IMPORTANT DATES

Submission deadline: April 18, 2018 (23:59 American Samoa Zone - UTC-11)

Notification of acceptance: May 15, 2018

Camera-ready due: May 27, 2018 (23:59 American Samoa Zone - UTC-11)

                         PAPER SUBMISSION

Research papers reporting original results as well as position papers
proposing novel and ground-breaking ideas pertaining to the workshop topics
are solicited.

Manuscripts must be in English with a maximum length of 6 pages for
research papers and 3 pages for position papers. Papers must be formatted
using the ACM SIG Standard (SIGCONF) proceedings template: Accepted papers will
be either presented as a talk or poster (to be determined).

At least one author of each accepted paper must attend the workshop and
present the paper.

Please submit your paper by using the on-line submission system via:

                        WORKSHOP CO-CHAIRS

Bamshad Mobasher, DePaul University, USA (mobasher [AT]
Robin Burke, DePaul University, USA (rburke [AT]
Michael Ekstrand, Boise State University, USA (michaelekstrand [AT]
Bettina Berendt, KU Leuven, Belgium (bettina.berendt [AT]

Michael D. Ekstrand — [log in to unmask]
Assistant Professor, Dept. of Computer Science, Boise State University
People and Information Research Team (PIReT) —

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