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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
RAIL-HH-10K Dataset
RAIL-HH-10K: The First Large-Scale Multi-Dimensional Safety Dataset
10,000 conversational tasks annotated across 8 ethical dimensions with 99.5% coverage. The first dataset enabling measurable improvements in AI safety and responsible behavior.
Fairness in Large Language Models
Comprehensive survey of fairness challenges and mitigation strategies in LLMs.
Safety Evaluation Frameworks
Novel approaches to evaluating AI safety across multiple dimensions.
Bias Detection in Generative AI
Methods for identifying and measuring bias in generative AI systems.
