Get help diagnosing and repairing your models for issues with bias, fairness, safety and accountability. We can perform hands-on analysis to understand the provenance of the bias and retrain models to meet improved objectives.

Write and review policies, model evaluation criteria, and processes to ensure that your team's models are accountable and well tested before they are released.

Help your machine learning engineers and data scientists learn about the policies and tools of responsible AI. Equally as important, educate your policy and product stakeholders about the technical challenges and opportunities of responsible AI. Develop and review responsible AI policies and playbooks for engineers and review committees.

Are you developing a user-facing model such as an LLM? We can help probe and test your model for bias and safety issues before your public release.

Sometimes accuracy is not enough: a model's reasons can be just as critical as its predictions. We can help you navigate the quickly growing body of Explainability research​ to find and implement the right methods for you.

• Civil rights auditing

• Responsible AI team charter development

• Public speaking