Responsible AI principles
Useful systems need explicit boundaries, reproducible checks and accountable operation.
Evaluate the actual task
Measure the relevant language, domain and failure modes. Narrow benchmarks do not support broad capability or safety claims. Include abstention and error analysis where an incorrect action matters.
Keep people in control
A model output is not permission to act. Validate tool requests and outputs, restrict privileges and require appropriate human review. High-stakes uses need domain expertise and additional safeguards.
Respect data and rights
Establish rights to use training and evaluation data. Minimize personal data. Keep evaluation sets separate from training material. Review model and dataset licenses rather than assuming that public access permits every use.
Disclose limitations
Record checkpoint, dataset, code and runtime versions. Make missing evidence visible. Commercial incentives must not alter a measured result. Do not publish synthetic fixtures as real model performance.