What features need to be changed to obtain a specific outcome?
This section will display the best explainable visualisations to indicate which features should be adjusted in order to enable users to make informed choices and optimize their path towards achieving a specific desired outcome.
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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.
