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Ensuring Ethical and Safe AI: Giskard's Open-Source Framework for Assessing AI Models


In the rapidly evolving field of artificial intelligence (AI), ensuring the reliability and safety of AI models is of utmost importance. With the enforcement of AI regulations like the AI Act in the EU and similar initiatives in other regions, companies engaged in AI model development are now required to demonstrate adherence to specific regulations and mitigate risks to avoid substantial fines.

Giskard is a French startup that has developed an open-source testing framework specifically designed for extensive language models (LLMs). The framework aims to help developers identify potential biases, security vulnerabilities, and the capacity of a model to generate harmful or toxic content. Giskard provides an open-source Python library that is compatible with various machine learning (ML) tools and integrates seamlessly into projects. They also offer additional products such as the AI Quality Hub for debugging and model comparison, and LLMon, a real-time monitoring tool for assessing LLM responses. Giskard is dedicated to promoting responsible and compliant AI model development in the face of increasing AI regulations.

How does Giskard's open-source framework evaluate AI models?

Giskard’s open-source framework evaluates AI models by providing developers with a comprehensive testing suite. This suite covers various aspects, including performance, hallucinations, misinformation, biases, data leakage, harmful content generation, and prompt injections.

The first step is to integrate Giskard’s open-source Python library into the ML project. This library is compatible with popular ML tools such as Hugging Face, MLFlow, Weights & Biases, PyTorch, Tensorflow, and Langchain. It seamlessly integrates into the project, allowing for easy testing and evaluation.

Once integrated, developers can generate a customized test suite tailored to their specific model’s end-use case. For example, if the model is a retrieval-augmented generation (RAG) model, Giskard can access relevant databases and knowledge repositories to enhance the relevance of the tests. This ensures that the tests are aligned with the model’s intended application.

The test suite can be integrated into the continuous integration and continuous delivery (CI/CD) pipeline. This means that the tests are automatically run during code iterations, ensuring regular assessments. Any deviations or issues detected during the testing process trigger scan reports in platforms like GitHub, providing developers with immediate feedback on potential problems.

In addition to the open-source testing framework, Giskard offers two other products. The AI Quality Hub is a premium offering that provides debugging and model comparison features. It also has the potential to incorporate regulatory features and generate documentation in the future.

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What are some of the features of Giskard’s open-source framework?

Giskard’s open-source framework offers several impressive features that make it a valuable tool for developers. Here are some of the key features:

1. Compatibility with popular ML tools: Giskard’s open-source Python library is compatible with a wide range of ML tools. This flexibility allows developers to seamlessly integrate the framework into their projects, regardless of the tools they are using.

2. Comprehensive test suite: Giskard helps developers generate a comprehensive test suite that covers various aspects of AI model performance and behavior. 

3. Tailored tests for specific use cases: Giskard understands that different AI models have different end-use cases. This customization ensures that the tests are tailored to the specific requirements of the model being developed.

4. Real-time monitoring tool: Giskard’s third product, LLMon, is a real-time monitoring tool that assesses the responses of large language models.

Types of AI models that can be evaluated by Giskard's open-source framework

Giskard’s open-source framework is designed to evaluate various types of AI models, particularly language models (LLMs). This includes but is not limited to:

Retrieval-Augmented Generation (RAG) models: Giskard’s framework can be integrated into projects involving RAG models, which combine retrieval-based and generative approaches for generating text.

Natural Language Processing (NLP) models: Giskard’s framework is compatible with NLP models developed using popular ML tools such as Hugging Face, MLFlow, Weights & Biases, PyTorch, Tensorflow, and Langchain.

Large Language Models (LLMs): Giskard’s framework is specifically designed to evaluate LLMs, which are advanced models capable of generating human-like text.

Models using OpenAI’s APIs: Giskard’s real-time monitoring tool, LLMon, is currently compatible with OpenAI’s APIs and LLMs, allowing developers to assess responses and detect issues before they reach users.



In conclusion, Giskard’s open-source framework is revolutionizing AI model evaluation for bias and security. With a commitment to embracing regulation, Giskard provides developers with comprehensive testing tools to ensure compliance and mitigate risks. Their AI Quality Hub and real-time monitoring tool, LLMon, enhance the development process by offering debugging capabilities and proactive issue detection. Giskard is poised to become a leading antivirus solution for large language models, promoting responsible and secure AI development.

Have you encountered challenges in evaluating AI models for bias and security? How important do you think it is for AI developers to adhere to specific regulations? Are you familiar with open-source testing frameworks like Giskard? If so, have you used them in your projects? Leave your insights.

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