Documentation Index

Fetch the complete documentation index at: https://docs.heymilo.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Cheat Detection

HeyMilo’s Cheat Detection helps recruiters identify potential cheating behaviors during candidate interviews. This guide explains how to configure the system and interpret the results.

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System Requirements

Before enabling cheat detection, ensure:

• Cheat detection is enabled for your workspace
• Candidates use a supported browser for web interviews
• Camera and microphone permissions are granted

If cheat detection is not available, contact support@heymilo.ai.

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Supported Browsers

Recommended (best experience) — Use one of these, kept up to date:

• Google Chrome: Latest stable version. Best tested; recommended for the most reliable experience.
• Microsoft Edge: Latest stable version. Full support and regularly tested.
• Mozilla Firefox: Latest stable version. Full support.
• Safari: Latest version on macOS and iOS. Use the latest Safari your device supports.

Also supported — Same or similar engines; should work when up to date:

• Opera, Opera GX, Brave, Samsung Internet (Android), Vivaldi, Arc (Chromium-based).

Not supported

• Internet Explorer (all versions). Use Edge or another modern browser.
• Very old or unpatched browsers.

General guidance: Desktop — Chrome, Edge, or Firefox on Windows/macOS/Linux, or Safari on Mac. Mobile — Safari on iOS or Chrome/Firefox/Samsung Internet on Android. Keep your browser updated.

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Where Cheat Detection Is Configured

Cheat detection can be configured in two places, depending on your workflow.

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Option 1: Workspace-Level Settings (Recommended Default)

Use this if you want consistent integrity settings across all interviews. Path: AI → Interview Integrity

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Settings Available

• Enable Cheat Detection — Turns integrity monitoring on for all new interviews in the workspace.
• Detection Threshold (0–100) — Controls how strict the system is when flagging events.
  • Lower values = less sensitive (fewer flags)
  • Higher values = more sensitive (flags more signals)
  • Recommended starting point: 70
• Detection Types — Select which signals to monitor:
  • Facial Behaviour
  • Multiple People
  • Phone Detection
  • AI / Scripted Answer
  • Unusual Delays
  • Tab Switching

Click Save Settings to apply changes.

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Option 2: Interview-Level Settings (Per Agent)

Use this if you need role-specific control. Path: Create or Edit Interviewer → Voice/Video Interview (workflow step) → Settings tab You’ll see the same options as workspace-level settings:

• Enable Cheat Detection
• Detection Threshold
• Detection Types

These settings apply only to that interviewer and override workspace defaults.

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What Each Detection Type Means

These signals are observational, not judgments.

• Facial Behaviour — Flags repeated or sustained face movement away from the screen that may indicate reading notes or external prompts.
• Multiple People — Detects the presence of more than one person in frame during the interview.
• Phone Detection — Flags when a mobile device appears in view during the interview.
• AI / Scripted Answer — Identifies response patterns that strongly resemble pre-written or AI-generated text, based on structure and delivery timing.
• Unusual Delays — Flags long or inconsistent response delays that may suggest off-screen assistance.
• Tab Switching — Detects when the candidate navigates away from the interview window during a response.

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Viewing Cheat Detection Results

Path: Interviewers → Candidate → Diagnostics & Analysis Scroll down to Cheat Detection Overview and you’ll see:

• Events grouped by detection type
• Timestamps for each flagged moment
• Confidence scores per event
• A synced player to jump directly to flagged points

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Understanding Confidence Scores

Confidence scores indicate how strongly a signal was detected, not intent.

• 80–100: Strong signal, review recommended
• 60–79: Moderate signal, check context
• Below 60: Likely environmental or accidental

Always review video and transcript context before drawing conclusions.

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Patterns Matter More Than Single Events

More weight should be given to:

• Repeated signals across the interview
• Multiple detection types occurring together
• Signals aligned with complex or high-stakes questions

Single, low-confidence events are often caused by lighting, camera angle, or natural movement.

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What Cheat Detection Does Not Do

• It does not automatically fail or reject candidates
• It does not score candidates higher or lower
• It does not analyze facial features, expressions, accent, or appearance
• It does not make hiring decisions

These signals exist to support review, not replace judgment.

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Best Practices

• Start with a threshold around 70
• Enable only the detection types you actually plan to review
• Always pair integrity signals with interview content
• Document how your team interprets signals
• Revisit settings as roles or hiring volume change

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Troubleshooting

No cheat detection data

• Feature may be disabled
• Interview may have been too short
• Camera permissions may not have been granted

Too many flags

• Decrease detection threshold
• Review candidate setup instructions

Inconsistent results

• Check browser compatibility
• Review lighting and camera placement

Example: A candidate with consistent 100% confidence “Eye Movement” detections from 1:20 to 8:03 likely looked away repeatedly, possibly at a phone or notes.

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Red Flags vs. False Positives

• High risk — Multiple detection types, repeated high-confidence incidents, aligned with tough questions.
• Possible false positives — Single low-score events, poor lighting, setup/connection issues.

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Additional resources

If you need help enabling or configuring cheat detection, contact your dedicated CS Manager or support@heymilo.ai.