The Future of FOIA: Course Corrections for the Digital Age

Prime, Tyler; Russomanno, Joseph

doi:10.1080/10811680.2018.1467156

Cited by 2 publications

(2 citation statements)

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“…There is a growing need for automatic sensitivity classification approaches to assist govern-1 https://www.legislation.gov.uk/ukpga/2000/36/contents ment reviewers to sensitivity review large collections of digital documents, to comply with FOI laws (Prime and Russomanno, 2018). However, automatically classifying FOI sensitivities is a challenging task (McDonald et al, 2014), since sensitivity is often context-dependent.…”

Section: Introductionmentioning

confidence: 99%

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

Narvala¹,

McDonald²,

Ounis³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual's salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar's test, p < 0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature (0.426 F 1 vs 0.413 F 1 ).

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Section: Introductionmentioning

confidence: 99%

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

Narvala¹,

McDonald²,

Ounis³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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“…A 2012 report by the National Security Archives, found that sixty-two out of ninety-nine government agencies have not updated their FOIA regulations since US Attorney General Eric Holder issued his March 19, 2009 FOIA memorandum to all heads of executive departments instructing them to make discretionary FOIA releases of documents that might be technically exempt from release. (National Security Archive 2012) Prime and Russomanno (2018), in an analysis of FOI requests made from 2007 to 2015, found that across multiple metrics, FOI requests have repeatedly failed to provide information to requesting parties. They conclude that despite efforts made to place "bandaids" on a system that's broken, FOI request processes need a major overhaul to meet the intention of the law and respond to increasing demands.…”

mentioning

confidence: 99%

Designing Engagement in Local News: Using FOIA Requests to Create Inclusive Participatory Journalism Practices

Mihailidis

Gamwell

2020

Journalism Practice

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This study explores how an engaged journalism process supported by participatory design practices can impact attitudes and perceptions of audience engagement in local journalism. Using MuckRock's Freedom of Information Act (FOIA) submission tool, journalists, journalism students, and community stakeholders engaged in participatory design workshops centered around pressing local issues and creative approaches to filing FOIA requests. This study re-imagines FOI requests not just as a way to locate source material for important local reporting, but as a visible process that invites the public to engage and understand how that reporting is doneand how anyone can use and benefit from the process. Findings show that design-driven engagement journalism practices can make public records culture more inclusive, engaging, and accessible, and support more collaborative and creative engagement between journalists and audiences.

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The Future of FOIA: Course Corrections for the Digital Age

Cited by 2 publications

References 0 publications

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

Designing Engagement in Local News: Using FOIA Requests to Create Inclusive Participatory Journalism Practices

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