{"id":23226,"date":"2025-05-13T15:17:48","date_gmt":"2025-05-13T15:17:48","guid":{"rendered":"https:\/\/www.imprima.com\/?p=23226"},"modified":"2025-05-29T14:14:41","modified_gmt":"2025-05-29T14:14:41","slug":"ai-driven-redaction-balancing-recall-and-precision","status":"publish","type":"post","link":"https:\/\/www.imprima.com\/blog\/ai-driven-redaction-balancing-recall-and-precision","title":{"rendered":"AI Driven Redaction – Balancing Recall and Precision"},"content":{"rendered":"\n

In our previous blog<\/a>, we explored how Large Language Models (LLMs) power the automatic redaction of sensitive information, emphasising the potential for high accuracy. Today, we\u2019ll dive deeper into the evidence behind this claim, focusing on the critical metrics of recall and precision in our LLM-driven Smart Redaction technology<\/a>.\u00a0<\/p>\n\n\n\n

<\/div>\n\n\n\n

Understanding Recall<\/span><\/p>\n\n\n\n

\"PII<\/figure>\n\n\n\n
<\/div>\n\n\n\n

As highlighted an earlier blog, Smart VDRs: It\u2019s All About Accuracy<\/em><\/a>, recall is the cornerstone of effective automated redaction. High recall ensures that nearly all sensitive terms requiring redaction are identified. Without it, the tool fails its primary purpose. We tested our LLM-driven Smart Redaction<\/a> technology on a set of data room documents, using 5-fold cross-validation to ensure statistically significant results. The experiment yielded an impressive average recall of 93%<\/strong>, confirming that our tool successfully identifies the vast majority of terms that need redaction.\u00a0<\/p>\n\n\n\n

<\/div>\n\n\n\n

The Role of Precision<\/span><\/p>\n\n\n\n

While recall is critical, precision is equally important to assess a model\u2019s accuracy. Precision measures the proportion of correctly redacted terms, or conversely, how few terms are incorrectly redacted (False Positives). High precision minimises unnecessary redactions, preserving document readability. <\/p>\n\n\n\n

Achieving perfect recall and precision simultaneously is impossible, even with cutting-edge AI. For example, redacting every word in a document would guarantee 100% recall but render the document useless due to 0% precision. Our LLM-driven tool optimises for high recall while maintaining strong precision, typically in the 80-90%<\/strong> range. In our recent test, precision was at the higher end of this spectrum. <\/p>\n\n\n\n

<\/div>\n\n\n\n

What This Means in Practice<\/span><\/p>\n\n\n\n

Let\u2019s break down the real-world impact. Suppose precision is at the lower end, say 80%<\/strong>. This means 20% of redacted terms were incorrectly flagged. If 5% of a document\u2019s terms require redaction (a reasonable estimate), only about 1%<\/strong> of the document\u2019s terms are erroneously redacted. This minimal error rate ensures the document remains highly readable. <\/p>\n\n\n\n

Moreover, our tools allow users to easily review and undo erroneous redactions. Stay tuned for next week\u2019s blog, where we\u2019ll explore these features in detail. <\/p>\n\n\n\n

<\/div>\n\n\n\n

Conclusion<\/span><\/p>\n\n\n\n

AI-driven redaction, powered by Large Language Models, offers significant time savings and reduces human error compared to traditional automation methods. As demonstrated last week, only LLM-based AI delivers the high accuracy required for reliable redaction. With recall above 90% and precision between 80-90%, our Smart Redaction<\/a> technology strikes an effective balance, ensuring both security and usability. <\/p>\n\n\n\n

Stay tuned for more insights next week! <\/p>\n\n\n\n

<\/div>\n\n\n\n

Are you looking for a VDR with fully integrated redaction software which leverages AI? Speak to our sales team<\/a> or check out our Smart Redaction page here<\/a>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

In our previous blog, we explored how Large Language Models (LLMs) power the automatic redaction of sensitive information, emphasising the potential for high accuracy. Today, we\u2019ll dive deeper into the evidence behind this claim, focusing on the critical metrics of recall and precision in our LLM-driven Smart Redaction technology.\u00a0 Understanding Recall As highlighted an earlier […]<\/p>\n","protected":false},"author":8,"featured_media":23230,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[169,170,171],"tags":[],"class_list":["post-23226","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-legal","category-vdr"],"yoast_head":"\nAI Driven Redaction - Balancing Recall and Precision - Imprima<\/title>\n<meta name=\"description\" content=\"Find out why AI Powered Redaction based on large language models provides unmatched accuracy and the ability to understand context.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.imprima.com\/blog\/ai-driven-redaction-balancing-recall-and-precision\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Driven Redaction - 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