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.

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 [email protected].

Where Cheat Detection Is Configured

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

Option 1: Workspace-Level Settings (Recommended Default)

Use this if you want consistent integrity settings across all interviews.

Path:

AI → Integrity

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 = more sensitive (more flags)

  • Higher values = less sensitive (only clear 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.

Option 2: Interview-Level Settings (Per Agent)

Use this if you need role-specific control.

Path:

Create or Edit Interviewer → Voice Interview → Settings

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.

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.

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

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.

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.

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.

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

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

• Increase 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.

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.

Additional resources

If you need help enabling or configuring cheat detection, contact [email protected] or use in-app chat.