Measure and Improve Your organisation's AI Responsibility
Comprehensive assessment of ethical AI practices, fairness, transparency, accountability, and responsible AI governance across 8 critical dimensions
Critical AI Responsibility Challenges
organisations face growing pressure to demonstrate responsible AI practices amid increasing regulatory requirements and stakeholder expectations
Algorithmic Bias
AI systems can perpetuate and amplify biases, leading to discriminatory outcomes that harm individuals and expose organisations to legal liability
73% of AI systems show measurable biasLack of Transparency
"Black box" AI decisions erode trust with customers, regulators, and stakeholders who demand explainability and understanding
85% want AI decision explanationsRegulatory Compliance
EU AI Act, NIST AI RMF, and emerging regulations require demonstrable AI responsibility practices and documentation
$35M+ potential regulatory finesAI Responsibility Index Framework
Structured assessment methodology to evaluate, benchmark, and improve your organisation's AI responsibility practices
Comprehensive Assessment
135 targeted questions across 8 dimensions evaluate your AI responsibility maturity with specific, actionable insights for improvement.
Maturity Benchmarking
5-level maturity model provides clear benchmarks and progression paths from Initial to Leading AI responsibility practices.
Regulatory Alignment
Assessment framework aligned with EU AI Act, NIST AI RMF, and ISO 42001 requirements for compliance-ready documentation.
Actionable Recommendations
Detailed, prioritised recommendations for improving AI responsibility practices based on your assessment results.
8 Responsibility Dimensions
Comprehensive evaluation across all aspects of responsible AI development, deployment, and governance
Ethical Leadership & Governance
Strategic leadership commitment to AI ethics with formal governance structures
Purpose-Driven Value-Aligned Innovation
Aligning AI innovation with organisational values and stakeholder needs
Human-Centricity & Agency
prioritising human well-being, autonomy, and oversight in AI systems
Safety, Robustness & Reliability
Ensuring AI systems are safe, resilient, and perform reliably
Transparency & Explainability
Clear communication of AI decision-making processes and outcomes
Social Impact & Global Equity
Evaluating societal effects and ensuring equitable AI outcomes
Environmental Sustainability
Managing the environmental footprint of AI systems and infrastructure
Vendor & Ecosystem Management
Managing AI vendor relationships and ecosystem partnerships
Assess Your AI Responsibility Maturity
Get a comprehensive evaluation of your organisation's AI responsibility practices with actionable recommendations for improvement