FATREC Workshop on Responsible Recommendation

@article{Ekstrand2017FATRECWO,
  title={FATREC Workshop on Responsible Recommendation},
  author={Michael D. Ekstrand and Amit Sharma},
  journal={Proceedings of the Eleventh ACM Conference on Recommender Systems},
  year={2017}
}
The first Workshop on Responsible Recommendation (FATREC) was held in conjunction with the 11th ACM Conference on Recommender Systems in August, 2017 in Como, Italy. This full-day workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. 
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References

SHOWING 1-10 OF 10 REFERENCES
Privacy Risks in Recommender Systems
Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.
A Survey of Explanations in Recommender Systems
  • N. Tintarev, J. Masthoff
  • Computer Science
    2007 IEEE 23rd International Conference on Data Engineering Workshop
  • 2007
This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to
Behaviorism is Not Enough: Better Recommendations through Listening to Users
TLDR
It is argued that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system will enable important developments in the future of recommender systems.
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
TLDR
This paper examines the effect of recommender systems on the diversity of sales, and shows how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences.
Fairness through awareness
TLDR
A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think
In December 2009, Google began customizing its search results for all users, and we entered a new era of personalization. With little notice or fanfare, our online experience is changing, as the
Auditing Search Engines for Differential Satisfaction Across Demographics
TLDR
A framework for internally auditing such services for differences in user satisfaction across demographic groups, using search engines as a case study is presented, and three methods for measuring latent differences inuser satisfaction from observed differences in evaluation metrics are proposed.
Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr
TLDR
Evidence of bias is found that gender and race are significantly correlated with worker evaluations, which could harm the employment opportunities afforded to the workers on TaskRabbit and Fiverr.
Certifying and Removing Disparate Impact
TLDR
This work links disparate impact to a measure of classification accuracy that while known, has received relatively little attention and proposes a test for disparate impact based on how well the protected class can be predicted from the other attributes.
Global Village or Cyberbalkans: Modeling and Measuring the Integration of Electronic Communities
TLDR
This paper formally defines a precise set of measures of information integration and develops a model of individual knowledge profiles and community affiliation, which suggest specific conditions under which improved access, search, and screening can either integrate or fragment interaction on various dimensions.