Open-Source for Open Minds: Build Fair & Ethical AI with Giskard

Giskard

ML Testing Library: Automatically detects vulnerabilities in AI models, from tabular models to LLMs, covering performance biases, data leakage, spurious correlations, hallucinations, toxicity, and security issues.
AI Quality Management System: Ensures compliance with AI regulations and facilitates collaborative AI quality assurance through dashboards and visual debugging tools.
LLMon (beta): Monitors LLMs (large language models) to detect potential issues like hallucinations, incorrect responses, and toxicity.
Standardized Methodologies: Utilizes established testing methodologies for optimal model deployment and risk mitigation.
SaaS and On-premise Deployment: Choose the deployment option that best suits your needs and infrastructure.

Developing and deploying reliable AI models: Identify and address potential biases, performance issues, and security vulnerabilities before deployment.
Complying with AI regulations: Ensure your AI models comply with industry standards and legal requirements.
Improving communication and collaboration: Facilitate clear communication and understanding of AI model strengths and weaknesses within teams.
Building trust in AI: Foster trust and transparency in your AI models by actively addressing potential risks and biases.
Research and development: Explore and experiment with AI quality assurance methods and contribute to the open-source platform.

Boosted AI Model Reliability: Minimize risks and ensure your AI models perform as intended without unexpected biases or errors.
Improved Transparency and Trust: Foster trust in your AI systems by actively addressing potential issues and demonstrating responsible development practices.
Reduced Regulatory Risk: Ensure compliance with AI regulations and avoid potential legal and ethical consequences.
Enhanced Collaboration and Communication: Facilitate clear communication about AI model capabilities and limitations within teams and stakeholders.
Open-source Development and Innovation: Contribute to and benefit from a community-driven platform for continuous improvement and advancement in AI quality assurance.

Enhanced AI Model Reliability: Identify and address potential biases, performance issues, and security vulnerabilities, minimizing risks and boosting trust in your AI.
Improved Transparency and Trust: Foster trust through actively addressing potential issues and demonstrating responsible development practices.
Reduced Compliance Risk: Ensure compliance with regulations and avoid legal and ethical consequences associated with biased or flawed AI models.
Enhanced Collaboration and Communication: Facilitate clear communication about AI model capabilities and limitations within teams and stakeholders.
Open-source Development and Innovation: Contribute to and benefit from a community-driven platform for continuous improvement and advancement in AI quality assurance.

Learning Curve: Requires initial exploration and understanding of AI concepts and the platform’s functionalities.
Limited Free Plan: The free plan restricts features and model size, potentially hindering extensive use for larger or complex models.
Technical Dependence: Relies on internet connectivity and understanding of basic system configurations for deployment.
Open-source Development: Potential for occasional bugs or dependence on community support for troubleshooting and updates.
Focus on Model Vulnerabilities: Primarily identifies potential issues, requiring additional measures for model improvement and optimization.