[Announcements] [CfP] The 5th FAccTRec Workshop on Responsible Recommendation @ RecSys 2022

[Nasim Sonboli]

(APOLOGIES FOR CROSS-POSTING)

[CfP] The 5th FAccTRec Workshop on Responsible Recommendation @ RecSys 2022
- Homepage: https://facctrec.github.io/facctrec2022/
- Submission: Aug 5, 2022

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?

## Submission Guidelines

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

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. Abstracts can be
omitted in the article.

Position papers connecting FAccTRec topics to recent events or public
discussions are also welcome.

### Research Papers

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.

## Paper format

We encourage you to format in the ACM manuscript / sigconf format with the
subsequent options.

\setcopyright{none}

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.

## Submission

Papers should be submitted from EasyChair. Please do not forget to choose
your type of submission: Position or Research.

## Important Dates

- 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)

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FAccTRec 2022 organizers https://facctrec.github.io/facctrec2022/committee/
E-mail: facctrec2022 at easychair.org
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