HƯỚNG DẪN KỸ THUẬT

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.

  • Đọc trong 3 phút
  • Cập nhật lần cuối
Trên trang nàyĐọc trong 3 phút
  1. Tổng quan
  2. Lặn sâu
  3. Tác động chiến lược
  4. The Future of Recall, Precision and Elusion Testing in Document Review
  5. Triển khai trong thế giới thực
  6. Rủi ro & lan can
  7. Lộ trình thực hiện
  8. Tiếp tục khám phá
  9. Câu hỏi thường gặp

Tổng quan

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

Lặn sâu

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.

Tác động chiến lược

Chi phí và ngân sách

Các quyết định về kiến ​​trúc sẽ thúc đẩy hiệu suất và chi phí vận hành trong nhiều năm.

Quyết định rõ ràng hơn

Giáo dục kỹ thuật giúp các nhóm chọn nhóm phù hợp chứ không chỉ nhóm mới nhất.

Kiểm soát chất lượng

Lựa chọn kỹ thuật tốt hơn làm giảm sự cố về độ tin cậy trong sản xuất.

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.

Triển khai trong thế giới thực

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.

Rủi ro & lan can

  • Tối ưu hóa một điểm chuẩn có thể che giấu những điểm yếu của hệ thống rộng hơn.

  • Chi phí cơ sở hạ tầng và bảo trì thường được đánh giá thấp.

  • Khoảng cách về bảo mật và khả năng quan sát có thể tăng lên khi hệ thống trở nên phức tạp hơn.

Lộ trình thực hiện

  1. Xác định các mục tiêu về độ trễ, chất lượng và chi phí trước khi triển khai.

  2. Điểm chuẩn trong điều kiện tải và dữ liệu thực tế.

  3. Giám sát thiết bị về lỗi, độ lệch và tác động của người dùng.

  4. Chuẩn bị đường dẫn khôi phục và ứng phó sự cố trước khi mở rộng quy mô.

Tiếp tục khám phá

Free newsletter

Get the daily AI briefing

Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.

One email each weekday. Unsubscribe in one click. We never sell or share your address.

Test yourself

Take the Recall, Precision and Elusion Testing in Document Review quiz

Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.

Bắt đầu bài kiểm tra

Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation

Câu hỏi thường gặp

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.