Our Auditing Methodology
Our AGIVP Platform employs a comprehensive methodology to evaluate AI systems against three core principles: Human Rights Alignment, Environmental Sustainability, and Fairness/Non-Bias.
Advanced AI Analysis
We employ advanced artificial intelligence techniques to analyze AI systems, creating a comprehensive evaluation that can identify nuanced ethical considerations across multiple dimensions.
Multi-dimensional Scoring
Each AI system receives individual scores across our three ethical principles, plus an overall ethical alignment score, allowing for nuanced understanding of strengths and areas for improvement.
Actionable Insights
Beyond scoring, our platform provides detailed analysis and specific, prioritized recommendations to help AI developers improve their systems' ethical alignment.
Audit Principles
1. Human Rights Alignment
We evaluate AI systems' alignment with fundamental human rights principles, ensuring they respect and protect individuals' dignity and freedoms.
Key Assessment Criteria:
- Privacy and Data Protection: Evaluating how the AI system collects, processes, and protects personal data, including consent mechanisms and data minimization practices.
- Individual Autonomy: Assessing whether the system respects user agency and avoids manipulation, coercion, or deception in its interactions with humans.
- Freedom of Expression: Analyzing if the system supports or restricts freedom of thought, expression, and association through its design or operation.
- Potential for Harm: Examining whether the AI system could be used for unjustified violence, oppression, or discrimination against individuals or groups.
Reference: UN Universal Declaration of Human Rights (1948)
2. Environmental Sustainability Alignment
We assess AI systems' environmental impacts and their potential to contribute positively to global sustainability goals.
Key Assessment Criteria:
- Energy Consumption: Evaluating the energy requirements for training and running the AI model, including computational resources and infrastructure needs.
- Carbon Footprint: Assessing the direct and indirect greenhouse gas emissions associated with the AI system throughout its lifecycle.
- Sustainability Contributions: Analyzing whether the AI system actively contributes to environmental goals such as climate action, biodiversity preservation, or resource efficiency.
- Transparency: Examining the level of disclosure provided about the environmental impact of the AI model and associated infrastructure.
Reference: Paris Climate Agreement (2015)
3. Fairness and Non-Bias Alignment
We evaluate AI systems for potential biases and discriminatory impacts across different demographic groups and contexts.
Key Assessment Criteria:
- Dataset Representation: Assessing whether the training data is diverse, representative, and balanced across demographic groups and relevant factors.
- Bias Detection: Testing for disparate performance or outcomes across racial, gender, age, cultural, and socio-economic dimensions.
- Systemic Discrimination: Analyzing if the system's design or implementation could perpetuate or amplify existing societal inequities or exclusionary patterns.
- Inclusion Promotion: Evaluating whether the AI system actively promotes equality and inclusion through its design, accessibility, and outcomes.
Reference: OECD AI Principles (2019); EU AI Act (2024)
The Audit Process
Information Collection
We gather comprehensive information about the AI system, including its purpose, technical capabilities, data sources, and intended applications.
Advanced AI Analysis
Our platform employs advanced AI techniques to analyze the provided information against our three ethical principles, identifying potential issues and strengths.
Scoring and Evaluation
Each aspect of the AI system is scored against relevant ethical criteria, generating both principle-specific and overall ethical alignment scores.
Issue Identification
Specific ethical issues are identified and categorized by principle area and severity, providing a prioritized view of concerns.
Recommendation Generation
Actionable recommendations are provided to address identified issues and improve the system's overall ethical alignment.
Results Delivery
A comprehensive audit report is generated, presenting all findings, scores, issues, and recommendations in an accessible and actionable format.