Abstract
Search engines are designed to make information accessible. However, some information should not be accessible, such as documents concerning citizenship applications or personal information. This sensitive information is often found interspersed with other potentially useful non-sensitive information. As such, collections containing sensitive information cannot be made searchable due to the risk of revealing sensitive information. The development of search engines capable of safely searching collections containing sensitive information to provide relevant and non-sensitive information would allow previously hidden collections to be made available. This work aims to develop sensitivity-aware search engines via two-stage cascading retrieval pipelines.
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References
Asadi, N., Lin, J.: Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures. In: Proceedings of SIGIR, pp. 997–1000 (2013)
Cossins, A.: Revisiting open government: recent developments in shifting the boundaries of government secrecy under public interest immunity and freedom of information law. Federal Law Rev. 23(2), 226–276 (1995)
Department for Science, Innovation and Technology, Department for Business and Trade, Office for Artificial Intelligence, Department for Digital, Culture, Media & Sport, and Department for Business, Energy & Industrial Strategy. Artificial intelligence sector deal. https://www.gov.uk/government/publications/artificial-intelligence-sector-deal (2019)
Jaillant, L., Caputo, A.: Unlocking digital archives: cross-disciplinary perspectives on ai and born-digital data. AI Soc. 37(3), 823–835 (2022)
Jaillant, L., Rees, A.: Applying AI to digital archives: trust, collaboration and shared professional ethics. DSH 38(2), 571–585 (2023)
Kang, J., Hustvedt, G.: Building trust between consumers and corporations: the role of consumer perceptions of transparency and social responsibility. J. Bus. Ethics 125, 253–265 (2014)
Oard, D.W., Shilton, K., Lin, J.: Evaluating search among secrets. In: Proceedings of EVIA@NTCIR (2016)
Sayed, M.F., et al.: A test collection for relevance and sensitivity. In: Proceedings of SIGIR, pp. 1605–1608 (2020)
Sayed, M.F., Oard, D.W.: Jointly modeling relevance and sensitivity for search among sensitive content. In: Proceedings of SIGIR, pp. 615–624 (2019)
McKechnie, J., McDonald, G.: Sara: a collection of sensitivity-aware relevance assessments (2024). arxiv.org/abs/2401.05144
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McKechnie, J. (2024). Cascading Ranking Pipelines for Sensitivity-Aware Search. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_41
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DOI: https://doi.org/10.1007/978-3-031-56069-9_41
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