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From:
Michael Ekstrand <[log in to unmask]>
Reply To:
Michael Ekstrand <[log in to unmask]>
Date:
Wed, 10 Apr 2019 16:21:47 -0600
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*Submission deadline*

For extended abstracts: *June 5th, 2019*

For full papers (for accepted abstracts): *Aug. 2nd, 2019*



*BACKGROUND AND SCOPE*

This special issue addresses research on responsible design, maintenance,
evaluation, and study of recommender systems. It is a venue for work that
has evolved out of recent workshops and conferences (e.g, FairUMAP, FATRec,
FATML, FAT*) on fair, accountable, and transparent (FAT) recommender
systems. In particular, it addresses what it means for a recommender system
to be responsible, and how to assess the social and human impact of
recommender systems. The questions addressed under each criterion are seen
as follows:

   - Fairness: what might ‘fairness’ mean in the context of recommendation?
   How could a recommender be unfair, and how could we measure such unfairness?
   - Accountability: to whom, and under what standard, should a recommender
   system be accountable? How can or should it and its operators be held
   accountable? What harms should such accountability be designed to prevent?
   - Transparency: what is the value of transparency in recommendation, and
   how might it be achieved? How might it trade off with other important
   concerns?



*TOPICS*

The special issue covers several aspects of FAT recommendation. Of
particular interest are case studies of successful FAT practices in domains
with large societal impact (e.g., healthcare, insurance, lending, news,
educational systems), but also with large financial impact (e.g., ecommerce
sites, travel booking sites, job search sites, dating sites, etc.). The
scope of the special issue includes but is not limited to:

*Modelling*

   - Fairness of user and item models (e.g., low confidence
   recommendations, misbalanced data, measures of diversity, low confidence
   recommendations)
   - Accountability of user and item models (e.g., accountability by or for
   different stakeholders, requirements on modeling to enable accountability)
   - Transparency of user and item models (e.g., explanatory needs for
   different user groups, explaining individual and global consumptions
   patterns)

*Recommendation*

   - Fairness of recommendations (e.g., trade-offs between criteria, bias
   for classes of items or users)
   - Accountability of recommendations (e.g., mechanisms for
   reporting/accounting, balancing filtering and completeness)
   - Transparency of recommendations (e.g., explanatory visualizations,
   user control, comparing explanatory aims)

*Methodologies*

   - Methodologies to assess Fairness (e.g., metrics for balance,
   diversity, and other social welfare criteria; evaluation simulations;
   assessing stakeholder specific bias)
   - Methodologies to assess Accountability (e.g., metrics and user studies
   of accountability mechanisms)
   - Methodologies to assess Transparency (e.g., metrics and evaluation
   frameworks for assessing the impact of interface or interaction strategies)

*Impacts*

   - Impacts of Fairness practices (e.g., balancing needs of different
   groups of users or stakeholders in recommender systems)
   - Impacts of Accountability practices (e.g., mechanisms for reporting
   data and models or decisions about them)
   - Impacts of Transparency practices (e.g., counterfactuals and what-if
   recommendations)



*PAPER SUBMISSION & REVIEW PROCESS*

Submissions will be pre‐screened for topical fit based on extended
abstracts. Extended

abstracts (up to three pages in journal format) should be sent to
[log in to unmask]

Deadline for extended abstracts: *June 5th, 2019*

Notification about extended abstracts: June 19th, 2019

Deadline for full manuscript submission: August 2nd, 2019

Notification 1st cycle: October 14, 2019

Deadline for revised manuscripts: December 12, 2019

Notification 2nd and final cycle: January 17, 2020

Deadline for camera-ready manuscripts: February 28, 2020



*GUEST EDITORS/CONTACT*

   - Nava Tintarev, Delft University of Technology, [log in to unmask]
   - Michael D. Ekstrand, Boise State University,
   [log in to unmask]
   - Robin Burke, University of Colorado, Boulder, [log in to unmask]
   - Julita Vassileva, University of Saskatchewan, [log in to unmask]



-- 
Michael D. Ekstrand — [log in to unmask]https://md.ekstrandom.net
Assistant Professor, Dept. of Computer Science, Boise State University
People and Information Research Team (PIReT) —
http://coen.boisestate.edu/piret/

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