Project Baba
← Back to products

RAG-Powered Bias Detection, Feedback, and Recommendation System

Overview

A RAG-based ethical AI review system that detects bias in user content and provides scores, explanations, and improvement recommendations.

AI-generated content can sometimes include unfair, biased, or sensitive patterns if the input content is not reviewed properly before generation. This becomes more important when users work with text or image-based content where ethical quality and fairness matter.

This project solves that problem by building a RAG-powered bias detection system that analyzes user content before generation. The system checks text and image inputs, searches relevant bias-related knowledge using vector databases, and evaluates the content for possible fairness issues.

It provides bias scores, clear explanations, and practical recommendations so users can improve their content before final generation. With RAG-based detection and feedback, the system helps make AI outputs more responsible, fair, and ethically aligned.

Key benefits

  1. Detects possible bias in text and image-based content
  2. Provides bias scores with clear explanations
  3. Uses RAG to improve detection and feedback quality
  4. Suggests recommendations for fairer content
  5. Supports responsible AI generation before final output

What's included

  1. Bias detection system for user content
  2. RAG-based feedback workflow
  3. Vector database search with Qdrant and Weaviate
  4. Bias scoring and recommendation module
  5. Django RESTful backend architecture

Use Cases

  1. Ethical AI content review
  2. Bias checking before content generation
  3. Text and image fairness analysis
  4. AI safety and compliance support
  5. Responsible content generation workflow

Key deliverables

  1. RAG-powered bias detection system
  2. Bias scoring engine
  3. Explanation and recommendation module
  4. Vector database integration
  5. Django REST API backend
  6. PostgreSQL-supported data structure
More products