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From:
Styliani Kleanthous <[log in to unmask]>
Reply To:
Styliani Kleanthous <[log in to unmask]>
Date:
Tue, 30 Mar 2021 20:36:07 +0300
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Apologies in advance for crossposting


=====================================================================

                        CALL FOR PAPERS

=====================================================================

Fourth Workshop on Fairness in User Modeling, Adaptation, and
Personalization (FairUMAP 2021)

At the ACM Conference on User Modeling, Adaptation, and Personalization
(UMAP 2021)

Utrecht, the Netherlands & Online, June 21-25, 2021



Workshop website: https://fairumap.wordpress.com/

Conference website: https://www.um.org/umap2021/index.php

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                       WORKSHOP DESCRIPTION

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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 YouTube have been heavily criticized for personalizing information
delivery too heavily at the cost of these other objectives.

Bias, fairness, and transparency 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 and user modeling are of primary importance. In particular,
it is essential to address these challenges from the standpoint of
understanding stereotypes in users’ behaviors and their influence on user
or group decisions.



The Workshop on Fairness in User Modeling, Adaptation, and Personalization
2021 aims to bring together experts from academia and industry to discuss
ethical, social, and legal concerns related to personalization and user
modeling with the goal of exploring a variety of mechanisms and modeling
approaches that help mitigate bias and achieve fairness in personalized
systems.

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                       TOPICS OF INTEREST

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

- Bias and discrimination in user modeling, personalization and
recommendation

- 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
data

- User modeling approaches that take fairness and bias into account

- User studies and other empirical 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

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                        IMPORTANT DATES

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Submission deadline: March 31, *April 5 2021 (23:59 American Samoa Zone -
UTC-11) - Extended*

Notification of acceptance: April 23, 2021

Camera-ready due: May 7, 2021 (23:59 American Samoa Zone - UTC-11)

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                        PAPER SUBMISSION

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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 8 pages for
research papers and 4 pages for position papers (with a maximum of one
additional page for references). Papers must be formatted according to the
new workflow for ACM publications. The templates and instructions are
available here: https://www.acm.org/publications/taps/word-template-workflow
.


Authors should submit their papers as single-column PDF files following the
templates:
LaTeX (use \documentclass[manuscript,review,anonymous]{acmart} in the
sample-authordraft.tex file for single-column):
https://www.acm.org/binaries/content/assets/publications/consolidated-tex-template/acmart-primary.zip
Overleaf (use \documentclass[manuscript,review,anonymous]{acmart} for
single-column):
https://www.overleaf.com/latex/templates/acm-conference-proceedings-master-template/pnrfvrrdbfwt
MS Word:
https://www.acm.org/binaries/content/assets/publications/taps/acm_submission_template.docx


Research papers should be submitted electronically as a single PDF file
through the EasyChair submission system  by selecting the track
"Workshop-Fair" (https://easychair.org/conferences/?conf=acmumap2021).
Accepted papers will be published by ACM and will be available via the ACM
Digital Library.



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.

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                     WORKSHOP Organizing Committee

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Bamshad Mobasher, DePaul University, USA

Styliani Kleanthous, Cyprus Center for Algorithmic Transparency (CyCAT),
Open University of Cyprus

Robin Burke, University of Colorado, Boulder, USA

Jahna Otterbacher, Cyprus Center for Algorithmic Transparency (CyCAT), Open
University of Cyprus

Bettina Berendt, KU Leuven, Belgium

Tsvi Kuflik, University of Haifa, Israel

Avital Shulner, University of Haifa, Israel


-- 


Styliani Kleanthous, Ph.D

CyCAT - Cyprus Center for Algorithmic Transparency

Open University of Cyprus

Phone: 22411904

web: http://www.cycat.io

LinkedIn: https://www.linkedin.com/in/styliani-kleanthous/

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