Toxicity Detection Module

Based on advanced neural network architectures like Transformers, the module captures long-range dependencies and complex contextual features. It is trained on diverse datasets and fine-tuned for specific types of harmful content.

Robustness Evaluation Module

Generates and tests adversarial samples using algorithms such as Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and more sophisticated attack strategies, ensuring model robustness.

Privacy Protection Assessment

Provides comprehensive privacy assessments and recommendations for securing data throughout the model's lifecycle.

Fairness Audit

Utilizes statistical methods and machine learning techniques to detect biases in training data and model outputs.

Content Safety Scan

Incorporates natural language processing techniques to detect and flag content that violates predefined safety standards.

Ethical Assessment

Evaluates transparency, explainability, and user privacy protection practices. Supports iterative updates to ethical standards.