How Sensitivity Classification Effectiveness Impacts Reviewers in Technology-Assisted Sensitivity Review

All government documents that are released to the public must first be manually reviewed to identify and protect any sensitive information, e.g. confidential information. However, the unassisted manual sensitivity review of born-digital documents is not practical due to, for example, the volume of documents that are created. Previous work has shown that sensitivity classification can be effective for predicting if a document contains sensitive information. However, since all of the released documents must be manually reviewed, it is important to know if sensitivity classification can assist sensitivity reviewers in making their sensitivity judgements. Hence, in this paper, we conduct a digital sensitivity review user study, to investigate if the accuracy of sensitivity classification effects the number of documents that a reviewer correctly judges to be sensitive or not (reviewer accuracy) and the time that it takes to sensitivity review a document (reviewing speed). Our results show that providing reviewers with sensitivity classification predictions, from a classifier that achieves 0.7 Balanced Accuracy, results in a 38% increase in mean reviewer accuracy and an increase of 72% in mean reviewing speeds, compared to when reviewers are not provided with predictions. Overall, our findings demonstrate that sensitivity classification is a viable technology for assisting with the sensitivity review of born-digital government documents

Natural Language Processing and Machine Learning as Practical Toolsets for Archival Processing

Peer ReviewedPurpose – This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach – The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings – Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value – Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows