技術指南

Recall, Precision and Elusion Testing in Document Review

Recall estimates how many responsive documents a review found; precision estimates how many documents labeled responsive are truly responsive.

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  1. 概述
  2. 深入探討
  3. 戰略影響
  4. The Future of Recall, Precision and Elusion Testing in Document Review
  5. 現實世界的實施
  6. 風險與防護欄
  7. 實施路線圖
  8. 不斷探索
  9. 常見問題

概述

Elusion examines responsive documents hidden in the set classified nonresponsive. Sampling supports estimates, not a guarantee of perfect production.

深入探討

In technology-assisted document review, recall is the proportion of all truly responsive documents that the process identifies. Precision is the proportion of documents identified as responsive that are truly responsive. A confusion matrix separates true positives, false positives, false negatives, and true negatives against a defined reference classification. These measures answer different questions: high recall can come with lower precision, and vice versa. Elusion estimates how many responsive documents are present in the population the system classified as nonresponsive. It can be easier to sample than the entire responsive population, but low elusion does not always establish high recall, especially when responsive documents are rare. EDRM’s statistical sampling guide warns that inference depends on prevalence and a valid sample, and that derived recall estimates do not automatically inherit the confidence level of their component estimates. Statistical random sampling can support quantitative estimates when the population, coding criteria, sample design, and uncertainty are documented. Judgmental review may help find examples or guide training, but it does not provide the same statistical conclusions. The result is an estimate tied to its sample and assumptions, not proof that every responsive document was found. Teams should define responsiveness with counsel, compare reviewers to a defensible reference standard, and explain limitations in any validation report. The specific protocol should fit the matter and any agreements or court orders.

戰略影響

成本與預算

多年來,架構決策決定著效能和營運成本。

更明確的決策

技術教育幫助團隊選擇正確的堆疊,而不僅僅是最新的堆疊。

品質管控

更好的工程選擇可以減少生產中的可靠性事故。

The Future of Recall, Precision and Elusion Testing in Document Review

As review platforms add analytics and generative features, defensible validation still depends on a clear population, documented criteria, and appropriate sampling. Teams may refine workflows as they learn more about the collection. Measurements help assess risk and workload, but they do not replace legal judgment or matter-specific agreements. Courts and parties may choose different protocols based on collection size, claims, and production needs. As models and search tools change, teams should explain their design and test behavior on representative data. Transparent estimates help discuss risk, but no single threshold resolves every legal dispute.

現實世界的實施

Counsel estimates whether the responsive set may contain important uncoded material.

A team samples documents coded nonresponsive to estimate elusion.

A reviewer compares machine coding with a documented human-coded reference set.

Parties agree on sampling method, confidence goals, and the review population.

風險與防護欄

  • 優化一項基準測試可以隱藏更廣泛的系統弱點。

  • 基礎設施和維護成本常常被低估。

  • 隨著系統變得更加複雜,安全性和可觀察性差距可能會擴大。

實施路線圖

  1. 在實施之前定義延遲、品質和成本目標。

  2. 在實際負載和資料條件下進行基準測試。

  3. 儀器監控錯誤、漂移和使用者影響。

  4. 在擴展之前準備回滾和事件回應路徑。

不斷探索

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常見問題

What is Recall, Precision and Elusion Testing in Document Review?

Recall estimates how many responsive documents a review found; precision estimates how many documents labeled responsive are truly responsive. Elusion examines responsive documents hidden in the set classified nonresponsive. Sampling supports estimates, not a guarantee of perfect production.

A sample shows 80 responsive documents out of 100 documents coded responsive. Which measure is 80%?

Precision asks what share of identified responsive documents are actually responsive.

A responsive document was classified nonresponsive. Which cell does it occupy in a confusion matrix?

The item is responsive in the reference coding but missed by the review.

Which sample most directly estimates elusion?

Elusion concerns responsive items among the nonresponsive population.

Why can a low elusion estimate fail to establish high recall when responsiveness is rare?

EDRM cautions that low elusion may be misleading when responsive prevalence is very low.

Which sampling approach supports a statistical claim about a population?

Statistical inference requires an appropriate selection design, not convenience selection.