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

UMUAI FatRec 2019

URL: https://tinyurl.com/umuai‐si-fatrec
Call For Paper Type: SI
H2 Index: 0
Submission Date: 2019-08-02
Notification Date: 2019-10-14
Final Version Date: 2019-12-12

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://tinyurl.com/umuai‐si-fatrec

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2024-06-02 ~ 2024-06-06

2024-08-19 ~ 2024-08-22

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

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