Responsible AI
Responsible AI
AI must solve a legitimate problem, stay accountable to a person and be evaluated before and after it goes live.
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Principles
- Purpose: AI must solve a legitimate business or user problem.
- Accountability: a human owner remains responsible for every deployed system.
- Transparency: clients understand material model and provider limitations and where AI is used.
- Privacy and security: sensitive data is handled deliberately and access is controlled.
- Evaluation: production AI is tested against defined quality and risk criteria.
- Human oversight: higher-risk decisions include appropriate review or escalation.
- Fairness: foreseeable discriminatory or harmful outcomes are considered.
- Reliability: failure, fallback and provider or model change are designed for.
- Traceability: important instructions, models, data sources and evaluations are versioned where appropriate.
- Continuous monitoring: material AI services are re-evaluated as models, prompts, data and context change.
Questions we aim to answer
For an AI system we deliver, Vitalos aims to be able to answer where your data goes, which model or provider processes it, whether it is used for provider training, where it is stored, what retention applies, what tools and permissions an agent has, how outputs are evaluated and what happens when the model fails.