8 Dimensions of Ethical AI
Comprehensive evaluation framework that ensures your AI meets the highest standards of responsibility.
Fairness
Measures and prevents bias to ensure equitable treatment across demographics.
Safety
Evaluates prevention of harmful or toxic content for user well-being.
Reliability
Assesses consistency and accuracy of AI responses.
Transparency
Evaluates clarity of AI decision-making and data usage communication.
Privacy
Checks protection of sensitive data and adherence to privacy standards.
Accountability
Evaluates traceability of AI decisions and error correction.
Inclusivity
Measures AI support for diverse users and accessibility.
User Impact
Assesses positive value and helpfulness of AI interactions.
Fairness
Measures and prevents bias to ensure equitable treatment across demographics.
Safety
Evaluates prevention of harmful or toxic content for user well-being.
Reliability
Assesses consistency and accuracy of AI responses.
Transparency
Evaluates clarity of AI decision-making and data usage communication.
Privacy
Checks protection of sensitive data and adherence to privacy standards.
Accountability
Evaluates traceability of AI decisions and error correction.
Inclusivity
Measures AI support for diverse users and accessibility.
User Impact
Assesses positive value and helpfulness of AI interactions.
From Our Research Lab
Cutting-edge datasets and frameworks powering the next generation of responsible AI
Sarvam AI Responsible AI Evaluation: Indian LLM Benchmark
212 adversarial prompts across 22 Indian-context categories evaluated against 3 Sarvam models using RAIL Score v2. sarvam-30b and sarvam-105b lead at 7.43/10; sarvam-m shows critical safety gaps.
Real harms from AI systems, verified and tracked.
A public catalogue of incidents where AI has caused serious harm: chatbot-linked deaths, wrongful arrests by facial recognition, deepfake fraud, algorithmic discrimination in welfare and hiring. Every entry is cross-checked against multiple top-tier sources.
incidents
cases
tracked