posted by organizer: navatintarev || 3670 views || tracked by 11 users: [display]

UMUAI FatRec 2019 : UMUAI Special issue on: Fair, Accountable, and Transparent Recommender Systems

FacebookTwitterLinkedInGoogle

Link: http://tiny.cc/umumuai_si_fatrec
 
When N/A
Where N/A
Abstract Registration Due Jun 5, 2019
Submission Deadline Aug 2, 2019
Notification Due Oct 14, 2019
Final Version Due Dec 12, 2019
Categories    recommender systems   fat
 

Call For Papers

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?

GUEST EDITORS/CONTACT

Nava Tintarev, Delft University of Technology, n.tintarev@tudelft.nl
Michael D. Ekstrand, Boise State University, michaelekstrand@boisestate.edu
Robin Burke, University of Colorado, Boulder, rburke@cs.depaul.edu
Julita Vassileva, University of Saskatchewan, jiv@cs.usask.ca

TOPICS

* Modelling
- Fairness of user and item models (e.g., low confidence recommendations, disbalanced 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 n.tintarev@tudelft.nl. Detailed instructions for paper submissions and updates will be posted online: https://www.tudelft.nl/ewi/over-de-faculteit/afdelingen/software-technology/web-information-systems/umuai_si_fatrecsys/

Related Resources

FATESys 2021   1st ACM SIGEnergy Workshop on Fair, Accountable, Transparent, and Ethical (FATE) AI for Smart Environments and Energy Systems
ECIR 2022   European Conference on Information Retrieval
MDPI Digital SI 2021   MDPI Digital (Free of charge) SI on White-box Artificial Intelligence
JDSA SI 2021   Springer Journal Special Issue on Data Science for Next-Generation Recommender Systems
XKDD 2021   3rd International Workshop on eXplainable Knowledge Discovery in Data Mining
BDCC 2021   Big Data and Cognitive Computing, Special Issue on Recommendation, Information Retrieval, and Exploratory Search
EXTRAAMAS 2021   EXplainable and TRAnsparent AI and Multi-Agent Systems
Special issue on Recommender systems 2021   Scopus/Springer Special issue: Data Science for Next-Generation Recommender Systems with International Journal of Data Science and Analytics
SeWeBMeDA 2021   Cancelled: 5th International workshop on Semantic Web solutions for large-scale biomedical data analytics