Explainable AI (XAI) bridges this gap, turning opaque processes into transparent, understandable insights.
Deep reinforcement learning and neural networks are powerful, but their complexity often obscures how they make decisions. Explainable AI (XAI) bridges this gap, turning opaque processes into transparent, understandable insights.
Feature importance highlights the inputs that most influence a model’s output. For instance, in financial predictions, it may reveal that volatility or interest rates are the key drivers behind a decision.
In reinforcement learning, we break down how the “reward function” guides the agent’s learning process, helping users see the logic behind each action and decision.
LIME simplifies complex models by approximating them locally, explaining individual predictions in a way that’s easy to understand.
SHAP assigns each feature a “contribution score” for a given prediction, offering a clear breakdown of how each input influenced the outcome.
We pair these methods with interactive, intuitive visualizations—heatmaps, contribution charts, and more—to demystify the decision-making process.
