posted by organizer: navatintarev || 2396 views || tracked by 9 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

RecSys@FLAIRS 2020   Recommender Systems Track at FLAIRS Conference
MAKE-exAI 2019   Machine Learning & Knowledge Extraction Workshop on explainable Artificial Intelligence
CfP Journal (SCI IF=2,5) 2019   Springer/Nature BMC MIDM Explainable AI in Medical Informatics and Decision Support
TEAAM 2019   Transparent, Explainable and Affective AI in Medical Systems
EXTRAAMAS 2019   International Workshop on Explainable Transparent Autonomous Agent and Multi-Agent Systems