Day 8: Fine-Tuning Truth Detection with Thresholds #smartdetection #uximprovement

SEO Title: Day 8: Fine-Tuning Truth Detection with Thresholds #smartdetection #uximprovement
Focus Keyphrase: AI lie detector
Meta Description: Learn how to fine-tune detection thresholds in your AI lie detector app to balance emotion-based and semantic-based truth evaluations for real-world use.

Table of Contents

Making Your AI Lie Detector More Accurate

Real users are messy. Some people sound nervous even when telling the truth. Some lie smoothly without hesitation. Today, we’ll fine-tune our AI lie detector so it better handles real-world edge cases.

Step 1: Adjust GPT Weight vs Emotion Weight

Previously, we merged scores like this:


truth_score = (gpt_score * 0.6) + (emotion_score * 0.4)

If you want to trust GPT’s semantic analysis more, increase its weight:


truth_score = (gpt_score * 0.7) + (emotion_score * 0.3)

If you want emotion (voice stress) to be more important, use 0.5 / 0.5.

Step 2: Customize Color Thresholds for UI Feedback

In your frontend recorder.js where we set colors:


// Old:
if (data.truth_score &gt; 70) { green }
else if (data.truth_score &gt; 40) { orange }
else { red }

// Fine-tuned:
if (data.truth_score &gt; 80) { green }
else if (data.truth_score &gt; 50) { orange }
else { red }

This new setting expects a higher standard for “trustworthiness” (80%+).

Step 3: Add Sensitivity Settings (Advanced)

Optionally, you can let users adjust how strict the lie detection should be:


// Example HTML:
Sensitivity: &lt;input id="sensitivity" type="range" min="0" max="100" value="70"&gt;

// JavaScript tweak:
const userSensitivity = document.getElementById('sensitivity').value;
if (data.truth_score &gt; userSensitivity) { green }
else if (data.truth_score &gt; (userSensitivity - 30)) { orange }
else { red }

Step 4: Tuning for Specific Scenarios

You might want different settings for:

🧑‍🏫 Public speaking practice → prioritize emotion score
🎭 Acting / deception games → prioritize contradiction score
🕵️ Interview training → balance both strictly

Why Threshold Tuning Is Crucial

Without tuning, your AI lie detector could produce lots of false positives (honest people looking guilty) or false negatives (smooth liars passing through). Fine-tuning creates a better UX and builds user trust.

Coming Tomorrow

In Day 9: Adding Session History and User Reports #sessiontracking #reportingux, we’ll store multiple analysis results into a history panel — allowing users to review past recordings and generate mini-reports.

Tags: #AIUX #LieDetection #ThresholdTuning #EmotionAnalysis

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