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Explainability

Definition

The degree to which a model's output can be explained in terms of its inputs and logic. Logistic regression and decision trees are inherently explainable; deep neural networks are not. Forensic applications prioritise explainability because the output must withstand expert cross-examination.

Explainable models
Logistic regression, decision trees
Low-explainability models
Deep neural networks
Forensic priority reason
Output must withstand expert cross-examination
Field
Fraud detection and data analytics

Common questions

Why would a forensic team choose a less accurate but more explainable model?+

An opinion that cannot be explained in terms a judge or jury can follow is vulnerable to exclusion or heavy discounting on cross-examination, so an interpretable model whose reasoning can be defended is often preferred over a marginally more accurate black box.

Does using an explainable model like a decision tree remove the need to validate it?+

No. Explainability describes whether the model's logic can be inspected and communicated, not whether it is accurate or reliable. A transparent model still needs testing against known data and a disclosed error rate before its output supports a conclusion.

Related terms

Anomaly Scoring
A numeric score assigned to each entity or transaction based on how different it is from the expected population, derived from multiple...
Isolation Forest
An unsupervised machine-learning model for anomaly detection. It builds random decision trees and scores each record by the average depth required to...
Logistic Regression (Supervised Fraud Model)
A classification model trained on historically labelled transactions (fraud vs. legitimate) to estimate the probability that a new transaction is fraudulent. Requires...
Network Analysis (Link Analysis)
A method that models entities (people, companies, accounts, addresses) as nodes and connections between them (shared attributes, transactions, ownership) as edges, then...
Timeline Reconstruction
The process of ordering digital events from multiple sources into a single chronological account. Requires normalising all timestamps to a common reference...

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