Pi 5 AI Fairness Research Kit — Quantify Face Detection Bias Across Skin Tones

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Face detection models power everything from surveillance to smartphone unlocking, yet they often perform unevenly across skin tones. This kit transforms a Raspberry Pi 5 into a dedicated research rig that benchmarks multiple open-source detection models against diverse faces, computing Equal Error Rates, demographic parity, and other fairness metrics. You will collect a controlled dataset using the Pi Camera Module 3, fine-tune pre-trained detectors, and generate audit reports that meet academic publication standards.

What You'll Build

You will assemble a portable edge-AI workstation that captures facial data under consistent lighting, runs inference with models like YOLOv8-Face, SCRFD, and RetinaFace, and logs per-skin-tone detection confidences. The outcome is a reproducible pipeline that calculates statistical bias metrics such as Average Precision disparity, False Positive Rate ratios, and the Fairness Discrepancy Rate. This is not a toy demo; it is a full fairness audit tool ready for your B.Tech major project or research paper submission.

What You'll Learn

• Designing a bias detection experiment with controlled image acquisition and annotation protocols
• Implementing fairness metrics (EER, FPR/FNR disparity, demographic parity difference) in Python
• Benchmarking multiple open-source face detectors on the same dataset for comparative analysis
• Deploying large models on Raspberry Pi 5 with NVMe acceleration to achieve real-time inference

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This is designed for B.Tech ECE/EEE final-year students tackling AI ethics capstone projects, M.Tech researchers at IITs, NITs, BITS Pilani, or VIT exploring algorithmic fairness, and participants of the Smart India Hackathon working on socially responsible AI themes. CBSE Class 11-12 students with advanced Python skills and an interest in computer vision will also find this a powerful introduction to real-world bias evaluation.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.