The 5th FAccTRec Workshop on Responsible Recommendation at RecSys 2022 is a venue for discussing social responsibility problems in maintaining, evaluating, and studying recommender systems. In this workshop, we welcome research and position papers about ethical, social, and legal issues brought by the development and the use of recommendations that will support a discussion on providing and evaluating socially responsible recommendations.
We currently plan for this workshop to be a hybrid workshop, with an in-person component in Seattle in addition to a virtual component. Further details will be forthcoming, but physical attendance in Seattle will be encouraged if possible but not necessary to participate in the workshop.
Topics of Interest
FAccTRec stands for Fairness, Accountability, and Transparency in Recommender Systems and aims to draw attention to these issues at ACM RecSys, as has been done in the broader computer science community through events such as FAccT conference. There are many potential aspects of responsibility in recommendation, including (but not limited to):
- Responsibility: what does it mean for a recommender system to be socially responsible? How can we assess the social and human impact of recommender systems?
- Fairness: what might ‘fairness’ mean in the context of recommendation? How could a recommender be unfair, and how could we measure such unfairness? How can we design systems to address specific fairness-related harms to users, providers, or other stakeholders?
- 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?
- Compliance: how should algorithms and especially recommendation algorithms be designed to adhere to the laws or regulations, such as the EU GDPR, the IEEE EAD, or the UK Data Ethics Framework? How should data collection be rethought to meet those new privacy standards? How to meet the requirements regarding transparency and explainability of algorithmic decisions.
- Safety: how can a recommender system distort users’ opinions? What is required to be resilient to such a distortion? What is the proper treatment of private or sensitive information when making recommendations?
We encourage submissions on the above topics. No official proceedings will be published because the focus of this workshop is a discussion about the directions to build and manage responsible recommender systems and provide feedback on early-stage research. All accepted papers’ manuscripts will be expected to be posted on arXiv.org by the authors, and an arXiv Index will index the accepted papers. We allow manuscripts that have already been published or are currently submitted to another venue, so long as arXiv publication is compatible with that venue’s requirements; already-published manuscripts should be accompanied by a cover abstract justifying their contribution specifically to FAccTRec.
Manuscripts must be submitted through EasyChair and will be reviewed by our program committee. The review process is single-blind; the authors’ names do not need to be anonymized. Presentations will be held in an oral or a poster style.
Position papers address one or more of the above themes or practical issues in building responsible recommendations. These could be either research systems or production systems in the industry. The number of pages should be limited to three (3) pages in the ACM manuscript format and two (2) pages in the ACM sigconf format, not including references and acknowledgements. Abstracts can be omitted in the article.
Position papers connecting FAccTRec topics to recent events or public discussions are also welcome.
Research papers present empirical or analytical results related to the social impact of recommender systems or algorithms. These could be explorations of bias in recommender systems (either live systems or sandboxed algorithms), explainability and transparency of recommender systems, experiments regarding the impact of the recommender on its users or others, etc. We will construe the topics broadly. The number of pages should be limited to ten (10) pages in the ACM manuscript format or six (6) pages in the ACM sigconf format, excluding references and acknowledgements.
We encourage you to format in the ACM manuscript / sigconf format with the subsequent options.
CCS class and keywords parts can be omitted.
However, we will not limit to this format and accept one of the ACM sigconf, the IEEE proceedings format, the NeurIPS format, and the ICML proceedings format. The number of words should be limited to 2000 words for position papers, and 3000 - 5000 words for research papers. Each display-style-equation is counted as 30 words, and each figure or table is counted as 200 words.
Papers should be submitted from EasyChair. Please do not forget to choose your type of submission: Position or Research.
- 2022-08-05: Paper submission deadline
- 2022-08-27: Author notification
- 2022-09-10: Final version upload
- TBA in 2022-09-18 - 2022-09-23: Workshop
TIMEZONE: Anywhere On Earth (UTC-12)