Infosys-Responsible-AI-Toolkit

The Infosys Responsible AI toolkit provides a set of APIs to integrate safety,security, privacy, explainability, fairness, and hallucination detection into AI solutions, ensuring trustworthiness and transparency.

Repositories and Installation Instructions

The following table lists the modules of the Infosys Responsible AI Toolkit. Installation instructions for each module can be found in the corresponding README file within the module's directory.

#	Module	Functionalities	Repositories
1	ModerationLayer APIs (Comprehensive suite of Safety, Privacy, Explainability, Fairness and Hallucination tenets)	To regulate the content of prompts and responses generated by LLMs	responsible-ai-moderationlayer,responsible-ai-moderationModel,responsible-ai-admin.
2	Explainability APIs**	Get Explainability to LLM responses, Global and local explainability for Regression, Classification and Timeseries Models	responsible-ai-llm-explain,responsible-ai-explainability,responsible-ai-model-detail,responsible-ai-reporting-tool, responsible-ai-moderationlayer.
3	Image Explainability	Image Explain offers detailed explanations for images generated by Large Language Models (LLMs).	responsible-ai-fairness,responsible-ai-llm.
4	Responsible-ai-LLM	LLM provides an implementation for generating images using a Large Language Model (LLM).	responsible-ai-llm.

** Endpoints for explainability are located in both the explainability and moderation layer repositories. Refer to the README files in these repositories for more details on specific features.

Please refer Features and Endpoints document for more details on endpoints and their usage.

Modules for the Responsible AI Toolkit Interface

The Responsible AI toolkit provides a user-friendly interface for seamless experimentation and alignment with various Responsible AI principles. The following APIs are required to activate and utilize the toolkit's UI. To access the toolkit through the interface, refer to the README files associated with the listed repositories.

#	Module	Functionalities	Repositories
1	MFE	An Angular micro frontend app serves as a user interface where users can easily interact with and consume various backend endpoints through independently developed, modular components.	responsible-ai-mfe
2	SHELL	A shell application in a micro frontend architecture acts as the central hub, orchestrating and loading independent frontend modules. It provides a unified user interface where users can interact with different micro frontends, consume backend endpoints.	responsible-ai-shell
3	Backend	A Python backend module focused on registration and authentication handles user account management, including user registration, login, password validation, and session management.	responsible-ai-backend
4	Admin	Supporting module which is used for configuring the main module. User can create recognizer, custom templates, configure Thresholds and map it to created account and portfolio.	responsible-ai-admin
5	Telemetry	A python backend module defining the various tenets structure for ingestion of the API's data into Elasticsearch indexes. It provided customizable input validation and insertion of data coming from tenets into elasticsearch, which can be further displayed using kibana.	responsible-ai-telemetry
6	File Storage	Python module that provides versatile APIs for seamless integration across multiple microservices, enabling efficient file management with Azure Blob Storage. It supports key operations such as file upload, retrieval, and updates, offering a robust solution for handling files in Azure Blob Storage.	responsible-ai-file-storage

Documentation

For technical details and usage instructions on the Infosys Responsible AI toolkit's features, please refer to the documentation.

Toolkit features at a glance

Generative AI Models

Safety, Security & Privacy	Model Transparency	Text Quality	Linguistic Quality
* Prompt Injection Score * Jailbreak Score * Privacy check * Profanity check * Restricted Topic check * Toxicity check * Refusal check	* Sentiment check * Fairness & Bias check * Hallucination Score * Explainability Methods:- Thread of Thoughts (ThoT)- Chain of Thoughts (CoT)- Graph of Thoughts (GoT)- Chain of Verification (CoVe)* Token Importance	* Invisible Text, Gibberish checks * Ban Code check * Completeness check * Conciseness check * Text Quality check* Text Relevance check * Uncertainty Score * Coherence Score	* Language Critique Coherence* Language Critique Fluency* Language Critique Grammar* Language Critique Politeness

Machine Learning Models

Security	Fairness	Explainability
* Simulate Adverserial Attacks* Recommend Defense Mechanisms	* Bias Detection Methods:- Statistical Parity Difference- Disparate Impact Ratio- Four Fifth's Rule- Cohen's D* Mitigation Methods:- Equalized Odds- Re-weighing	* Global Explainability using SHAP* Local Explainability using LIME

Upcoming Features

• Multi-lingual support for FM-Moderation Guardrails
• Red Teaming using Datasets and Garak based Technique & Libraries
• Counterfactual explanation for ML model prediction

We appreciate your feedback and aim to keep you updated on our plans regularly. This approach ensures we're prioritizing the right tasks and enables you to make informed decisions based on our development roadmap.

Explore the roadmap »

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Note: These API-based guardrails are optimized for Azure OpenAI. Users employing alternative LLMs should make the necessary client co