AI Assessor
Our AI Model Assessment Tool provides a robust framework for evaluating AI models across key dimensions like fairness, transparency, security, etc.
How AI Assessor Works?
It assesses the trained AI model and the dataset for below mentioned parameters.
Responsible Use
Our AI assessment tool evaluates models for responsible use, particularly in ensuring fairness and mitigating bias. Leveraging more than 12 developed metrics, we analyze models and datasets to detect and quantify potential biases.
Explainability
Our AI assessment tool evaluates models for explainability and interpretability, focusing on building robust and transparent systems. Developed 10+ mertics helps us in systematically analyzing models to assess how well their decisions can be understood.
Safety and Security
Our AI assessment tool evaluates models for safety and security, using the OWASP Top 10 vulnerabilities as a framework to assess how vulnerable the models are. We perform different kinds of attack on models and datasets, and analyze the risks.
Privacy
Our AI assessment tool assesses privacy by employing membership inference attacks to identify potential privacy leaks. Using black-box and white-box attack, we thoroughly examine models for vulnerabilities that could expose sensitive data.
The tool integrates the Risk Assessment and Quantification Methods in the overall assessment.
Risk Quantification and Management
The tool integrates risk assessment and quantification methods into its overall evaluation process, providing a comprehensive analysis of trained AI models and datasets. By measuring risks across areas like fairness, explainability, security, and privacy, it ensures a thorough assessment and helps organizations mitigate potential vulnerabilities effectively. Additionally, we implement risk management in line with the ISO 23894 standard, ensuring a globally recognized approach to AI risk mitigation.
The tool also integrates the Regulatory Alignment for AI Models and Datasets.
Regulatory Compliance
The tool maps its assessments to regulatory compliance standards, ensuring that AI models and datasets meet legal and ethical requirements. By integrating risk assessment and quantification, it provides organizations with a thorough evaluation aligned with industry regulations, promoting responsible and compliant AI use.
