PRZEWODNIK techniczny

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.

  • 3 minuty czytania
  • Ostatnia aktualizacja
Na tej stronie3 minuty czytania
  1. Przegląd
  2. Głębokie nurkowanie
  3. Wpływ strategiczny
  4. The Future of Recall, Precision and Elusion Testing in Document Review
  5. Implementacja w świecie rzeczywistym
  6. Zagrożenia i poręcze
  7. Plan wdrożenia
  8. Odkrywaj dalej
  9. Często zadawane pytania

Przegląd

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

Głębokie nurkowanie

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.

Wpływ strategiczny

Koszt i budżet

Decyzje dotyczące architektury wpływają na wydajność i koszty operacyjne przez lata.

Jaśniejsze decyzje

Edukacja techniczna pomaga zespołom wybrać odpowiedni stos, a nie tylko najnowszy.

Kontrola jakości

Lepsze wybory inżynieryjne zmniejszają liczbę incydentów związanych z niezawodnością w produkcji.

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.

Implementacja w świecie rzeczywistym

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.

Zagrożenia i poręcze

  • Optymalizacja jednego testu porównawczego może ukryć szersze słabości systemu.

  • Koszty infrastruktury i utrzymania są często niedoszacowane.

  • W miarę jak systemy stają się coraz bardziej złożone, luki w bezpieczeństwie i obserwowalności mogą się zwiększać.

Plan wdrożenia

  1. Przed wdrożeniem zdefiniuj docelowe opóźnienia, jakość i koszty.

  2. Test porównawczy w realistycznych warunkach obciążenia i danych.

  3. Monitorowanie przyrządu pod kątem błędów, dryftu i wpływu użytkownika.

  4. Przed skalowaniem przygotuj ścieżki wycofywania zmian i reakcji na incydenty.

Odkrywaj dalej

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Często zadawane pytania

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.