AI-Hunter
AI-Hunter is an AI bias detection platform that analyzes machine learning models for biases and ethical issues. It scans models for signs of unfairness, lack of explainability, and potential harms.
ai
machine-learning
bias-detection
fairness
explainability
ethics
AI-Hunter: AI Bias Detection Platform
Detects biases in machine learning models, ensuring fairness, explainability, and safety for your applications.
What is AI-Hunter?
AI-Hunter is an AI auditing platform designed to detect biases, unfairness, and transparency issues in machine learning systems. It provides comprehensive analysis of machine learning models across various criteria related to ethics and responsible AI.
Key features of AI-Hunter include:
- Bias detection - Identifies various biases like gender, racial, age-related biases in dataset and ML models.
- Fairness analysis - Checks for algorithmic unfairness issues that may lead to discriminatory outcomes.
- Explainability analysis - Assesses model interpretability and explanations to ensure sufficient transparency.
- Data quality analysis - Evaluates dataset balance, representation, labeling issues.
- Model card generation - Automatically creates model cards documenting metrics, performance, intended use cases.
- Mitigation guidance - Provides bias mitigation guidance tailored to identified issues.
By auditing AI system with AI-Hunter, organizations can detect potential problems early and address issues around ethics, fairness, explainability before deployment. It works with all major ML frameworks like TensorFlow, PyTorch, Keras.
AI-Hunter Features
Features
- Bias detection
- Explainability analysis
- Potential harm identification
- Model scanning
- Comprehensive reports
Pricing
- Subscription-Based
Pros
Comprehensive analysis of machine learning models
Helps identify and mitigate biases and ethical issues
Improves model transparency and accountability
Supports responsible AI development
Cons
Requires technical expertise to interpret the reports
May not detect all types of biases or ethical issues
Limited to the analysis of machine learning models
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