Audit

As explained in Effective Data Analysis

An audit is a systematic examination of a system, process, or output against defined requirements or evaluation criteria.

An audit systematically examines an automated system’s outputs against defined fairness criteria. In hiring and promotion, it helps reveal whether rankings, ratings, or selections differ across protected groups. section 8.2.2

A practical bias audit asks whether automated decisions:

• Produce different candidate rankings or ratings by race, ethnicity, sex, or intersecting protected groups. section 8.2.2
• Select candidates at different rates across protected classes. section 8.2.2
• Create downstream effects that stakeholders would not otherwise anticipate. section 8.2.2

Hiring-system audit

Examine a vendor’s candidate quality score, an internal keyword-matching system, or machine-learning ranking tool, then compare scoring and selection outcomes across protected groups. section 8.2.2

Figure 8.5 The categorization system developed by the data science and analytics team can streamline the work of the customer support team. Still, it may have an unanticipated negative effect on certain groups of customers.

Use the figure to connect operational efficiency with the possibility of unintended negative effects on particular groups. section 8.2.2

How Effective Data Analysis covers it

How deep it goes

Frames bias through scoring and selection comparisons but does not provide a complete audit procedure or statistical test.

Examples

Uses candidate-ranking software, keyword rules, quality scores, and automated rejection thresholds as audit targets.

What it assumes

Assumes familiarity with automated decision tools and protected-group categories while framing audit responsibility for analysts.

What you can do after

Diagnose/debug with it by comparing automated rankings, ratings, and selection rates across protected groups.

How it connects

Connects audit with automated systems, machine learning, bias, protected classes, and stakeholder decisions.

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Also explained in

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  Architecting an Apache Iceberg LakehouseAuditing is the systematic examination and recording of data and system activities to support accountability, compliance, and operational assurance.
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  MongoDB 8.0 in Action, Third EditionAuditing is the recording and review of security-relevant database activity for accountability and compliance.
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  100 SQL Server Mistakes and How to Avoid ThemAuditing is the systematic recording and review of security-relevant actions and events for accountability and investigation.
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  Think Like a CTOAn audit is a systematic examination of records, controls, or activities to verify compliance, accountability, or correctness.

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Related concepts

• data integrityaddresses · Data integrity is the property that data remains accurate, complete, consistent, and valid, with changes made only through authorized actions.
• bias detectionhas part · Bias detection is the systematic assessment of data, decisions, or system behavior to identify distortions or unjustified disparities across groups or conditions.
• monitoringuses · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
• alertingproduces · Alerting is the generation and delivery of notifications when monitored conditions or events meet defined criteria or require attention or action.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.