XAI - Explainable KI

What is meant by XAI - Explainable KI?

XAI, or Explainable Artificial Intelligence, refers to techniques and methods aimed at making the decisions and predictions of AI models more understandable and interpretable for humans. The goal of XAI is to promote transparency and trust in AI systems by revealing the underlying processes and mechanisms.

Typical functions of software in the area of "XAI - Explainable AI":

1. Transparent Model Descriptions: Providing understandable explanations about how the AI model works and which algorithms are used.
2. Decision Explanation: Detailed representation of the decision-making processes and pathways that led the model to a prediction or classification.
3. Visualizations: Graphical representations that clarify the internal processes and decision paths of the model, such as decision trees, heatmaps, or feature importance diagrams.
4. Feature Importance Analysis: Analysis and presentation of the most important features influencing the model’s predictions and decisions.
5. Interactive Explanations: Tools that allow users to understand and test the impact of changes in input data on the model’s predictions.
6. Model Comparison: Functions that enable the comparison of explanations and performances of different models to select the most suitable one.
7. Natural Language Explanations: Providing explanations in understandable, natural language that are easily accessible to non-technical users.
8. Audit Trails: Logging and tracking the model’s decisions and predictions for review and compliance purposes.
9. Anomaly Detection: Identification and explanation of unusual or unexpected results to uncover possible errors or biases in the model.
10. User-Friendly Interfaces: Intuitive and user-friendly interfaces that allow users to easily understand and utilize the explanations and analyses.