Pattern library

Turns the factors behind an adverse decision into specific, realistic steps a person can actually take.

Makes an autonomous agent’s actions visible before, during, and after it runs, so oversight stays possible.

Makes AI involvement visible at the moment it matters, from the first message of a chat to a decision letter.

Marks AI-generated files and text so their origin travels with them, long after they leave the interface.

Shows the top factors behind the system’s assessment in plain language, and whether each one helped or hurt it.

Keeps a tamper-evident record of inputs, model versions, and human steps that others can actually read.

Signals how sure the system is about an output, in a form non-technical users can read at a glance.

Makes personal data use understandable at the point of decision, so people know what happens to their data and any consent is informed rather than just collected.

Gives people a working route to challenge a decision and get a human review with a documented response.

Shows the smallest realistic change that would have produced a different result, making the decision boundary visible.

Shows where the data behind an AI recommendation or decision came from, how current it is, and whether it fits the case at hand.

Lays out a decision as a chronology of steps and actors, from application through review to outcome.

Shows how the system performs across different populations, making bias visible and open to challenge.

Explains a result by comparing it to cases the user can recognise, building intuition through analogy rather than abstraction.

Structures an explanation in depth layers, so a quick answer comes first and the full basis stays one step away.

Gives users a low-friction way to report errors and disagree, and tells them what happened to their input.

Distinguishes generated claims that rest on retrievable evidence from those the model may have invented.

Lets the people who decide review, question and override the system’s recommendation, with every decision on the record.

Lets users interrogate a result directly, probing single factors and testing what-ifs in their own words.

States what the system was built and validated for, and flags clearly when a case falls outside that.

Documents a system’s purpose, data, performance, and limits in a form non-engineers can actually use.

Shows the documents the system looked up for an answer and which sentences they support, so people can check claims against the originals.

Adapts the depth, language, and available actions of an explanation to who is reading it and what they may do.

Signals when a model’s reliability may have drifted since deployment, so trust tracks current performance.