Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR)

@article{Olteanu2019WorkshopOF,
  title={Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR)},
  author={Alexandra Olteanu and Jean I. Garcia-Gathright and M. de Rijke and Michael D. Ekstrand},
  journal={Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2019}
}
This workshop explores challenges in responsible information retrieval system development and deployment. The focus is on determining actionable research agendas on five key dimensions of responsible information retrieval: fairness, accountability, confidentiality, transparency, and safety. Rather than just a mini-conference, this workshop is an event during which participants are expected to work. The workshop brings together a diverse set of researchers and practitioners interested in… 
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6 Citations
Background Citations
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Methods Citations
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6 Citations

Citation Type

Citation Type

FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval
TLDR
The purpose of the SIGIR 2019 workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety (FACTS-IR) was to explore challenges in responsible information retrieval system development and deployment and draft an actionable research agenda.
FACTS-IR
TLDR
The purpose of the SIGIR 2019 workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety (FACTS-IR) was to explore challenges in responsible information retrieval system development and deployment and draft an actionable research agenda.
Some Reflections on the Use of Structural Equation Modeling for Investigating the Causal Relationships that Affect Search Engine Results
TLDR
Whether Structural Equation Modeling (SEM) can be a useful methodology to investigate the causal relationships between the variables describing the content representation and retrieval processes of search engines and recommender systems is discussed.
When Are Search Completion Suggestions Problematic?
TLDR
This study explores when system-provided suggestions might be perceived as problematic through a series of crowd-experiments where they are systematically manipulate: the search query fragments provided by users, possible user search intents, and the list of query completion suggestions.
ExDocS: Evidence based Explainable Document Search
TLDR
It is found that ExDocS provides an explanation for rankings that are understandable and complete but the explanation comes at the cost of a drop in ranking quality, in line with the “no free lunch” theorem.
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
TLDR
This work proposes a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities and shows that the proposed entity-relation-entity embedding approach can significantly improve the effectiveness of sensitivity classification.

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