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FAccTRec 2020 : The 3rd FAccTRec Workshop on Responsible Recommendation
FAccTRec 2020 : The 3rd FAccTRec Workshop on Responsible Recommendation

FAccTRec 2020 : The 3rd FAccTRec Workshop on Responsible Recommendation

Rio de Janeiro
Event Date: September 26, 2020 - September 26, 2020
Submission Deadline: July 13, 2020
Notification of Acceptance: August 06, 2020
Camera Ready Version Due: September 03, 2020



About

The 3rd FAccTRec Workshop on Responsible Recommendation at Recys 2020 is a venue for discussing problems of social responsibility in building, maintaining, evaluating, and studying recommender systems, including but not limited to issues of fairness, accountability, and transparency (FAccT). The importance of these problems are now increasing due to the empowerment of social networking technologies and the change of social environment, such as the enforcement of the EU General Data Protection Regulation and broader awareness of social issues in informaiton distribution. In this workshop, we welcome research and position papers about ethical, social, and legal issues raised by the development and the use of recommendation. The workshop will include presentations of these papers and discussion on a research agenda for socially responsible recommendation.


Call for Papers

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 machine learning 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?
  • 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 the laws or regulations, such as the EU GDPR or the IEEE EAD? How should data collection be rethought to meet those new privacy standards ? How to meet the requirements in terms of 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 a proper treatment of private or sensitive information when making recommendations?



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Summary

FAccTRec 2020 : The 3rd FAccTRec Workshop on Responsible Recommendation will take place in Rio de Janeiro. It’s a 1 day event starting on Sep 26, 2020 (Saturday) and will be winded up on Sep 26, 2020 (Saturday).

FAccTRec 2020 falls under the following areas: RECOMMENDER SYSTEM, ETHICS, etc. Submissions for this Workshop can be made by Jul 13, 2020. Authors can expect the result of submission by Aug 06, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Sep 03, 2020 to the official website of the Workshop.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the FAccTRec 2020

  • Short Name: FAccTRec 2020
  • Full Name: The 3rd FAccTRec Workshop on Responsible Recommendation
  • Timing: 09:00 AM-06:00 PM (expected)
  • Fees: Check the official website of FAccTRec 2020
  • Event Type: Workshop
  • Website Link: https://facctrec.github.io/facctrec2020
  • Location/Address: Rio de Janeiro


Credits and Sources

[1] FAccTRec 2020 : The 3rd FAccTRec Workshop on Responsible Recommendation


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