How confident is the system about the forecast accuracy?
In this section, the best explainable visualisations to check the predictions’ level of confidence are displayed. Specifically, this section serves as a valuable resource for evaluating the robustness of predictions and enhancing your overall comprehension of the predictive outcomes.
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Description
Example from XMANAI
A textual representation can be used to facilitate AI model explainability by converting complex model outputs into human-readable text. Techniques like natural language processing and attention mechanisms help highlight key features and decision-making processes, providing concise and interpretable explanations. This promotes transparency and trust in AI systems for effective communication with end-users.
A text-based explaination is used by a XMANAI demonstrator to illustrate how changing the value of a specific feature will change the outcome of the prediction in a what-if scenario forecast. The user can define specific what-if scenario, changing value of some features and the results is visualised with a simple textual example.
