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# Explanation workflows

Explanation workflows show how the platform explanation components can be combined to support higher-level analysis.

The explanation components describe model behaviour at different levels: predictions, attributions, activations, rules, module dependency plots, strengths and weaknesses, histograms, anomalies, and context comparisons. Workflows combine these components to investigate, monitor, justify, optimise, or govern model behaviour.

## Workflow areas

| Workflow                                                                                       | Description                                                                                                                               |
| ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| [Model behaviour](./model-behaviour)                                                           | Use rules and module dependency plots to inspect trends, behaviour changes, conformance to expectations, and segment behaviour over time. |
| [Identifying bias](./identifying-bias)                                                         | Use control swaps, attributions, and module dependency plots to detect bias in predictions or in the model.                               |
| [Justifying attribution explanations](./justifying-attribution-explanations)                   | Use activated rules and their expressions to explain why an attribution was produced.                                                     |
| [Identifying data segments](./identifying-data-segments)                                       | Use rule conditions to understand the regions of data that induction found to behave similarly.                                           |
| [Identifying and addressing low data coverage](./identifying-and-addressing-low-data-coverage) | Use histograms and activations to find sparse regions, then inspect their behaviour with MDPs and rules.                                  |
| [Model monitoring](./model-monitoring)                                                         | Use activation data to detect deviations from expected behaviour, data drift, and potential intrusion or security events.                 |
| [Model optimisation](./model-optimisation)                                                     | Use global explanations such as feature importance and module importance to guide feature selection and module creation.                  |
| [Real-time explanations](./real-time-explanations)                                             | Use the Results view to receive predictions and explanation data at inference time.                                                       |
| [Audit logs](./audit-logs)                                                                     | Use explanation results as records for transparency, accountability, compliance, and forensic reconstruction.                             |

## How workflows use explanation components

Each workflow uses one or more explanation components.

| Component                | Common use                                                                              |
| ------------------------ | --------------------------------------------------------------------------------------- |
| Attributions             | Explain how much features, interactions, or modules contributed to a prediction.        |
| Activations              | Identify the partitions and rules used by a query, segment, dataset, or deployed model. |
| Rules                    | Show the conditions and local model expressions behind behaviour.                       |
| Module dependency plots  | Visualise module behaviour and trends across feature values or interactions.            |
| Histograms               | Inspect data distribution and coverage.                                                 |
| Strengths and weaknesses | Add confidence context through strength and uncertainty.                                |
| Context explanations     | Compare a query with swaps, similar cases, dissimilar cases, or related cases.          |
| Results view             | Capture prediction and explanation output in real time.                                 |

## From explanation to action

Explanation workflows can be used to move from inspection to action.

For example, a team may use histograms and activations to find low data coverage, inspect the corresponding module behaviour with MDPs and rules, and then decide whether to collect more data, adjust a workflow, or route affected decisions to a human review process.

Similarly, a team may use control swaps to identify bias in a prediction, compare the attribution deltas, and decide whether to leave the prediction as is, correct the bias automatically, or de-automate the decision for human review.