AI Explainability Toolkit

Empower teams to produce transparent, trustworthy AI experiences through consistent explainability practices.

Define explainability goals aligned to audience needs, model use cases

Segment explanations for end-users, domain experts, regulators, and internal reviewers.

Techniques & Patterns

live demo · related simulation● LIVE

Offer feedback mechanisms to capture user trust and understanding.

Leverage explainability libraries, testing frameworks, and documentation automation.

Model Explainability APIs

Frequently asked questions

What allows stakeholders to experiment with inputs and observe outputs safely?

Allow stakeholders to experiment with inputs and observe outputs safely.

How is governance and evidence managed within mysimulator.uk?

Mysimulator.uk utilizes a structured approach to governance, meticulously tracking explainability requirements for each AI system alongside comprehensive documentation of supporting evidence demonstrating compliance with those requirements.

What role do review boards play in evaluating explanation quality and bias detection?

Review boards are established to rigorously assess the quality of explanations generated by the system, proactively identify potential biases within the AI models, and incorporate valuable user feedback to continuously improve the simulation's reliability and transparency.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation