In an era where chatbots are essential to digital interactions, understanding their decision-making processes is key to building user trust. Shradha Kohli‘s article explores innovative Explainable AI (XAI) techniques that enhance chatbot transparency, making these systems more user-centric and accountable. Her insights are especially relevant as AI-driven communication grows in both prevalence and complexity.
The Need for Transparent Chatbots
As chatbots evolve into complex AI models, decision-making opacity can erode trust. Explainable AI (XAI) clarifies chatbot logic, aiding users and developers in addressing biases and errors for more reliable interactions, especially in sensitive fields.
Key XAI Techniques in Chatbot Contexts
Three key XAI techniques LIME, SHAP, and counterfactual explanations offer unique insights into chatbot decision-making, each highlighting distinct strengths and limitations in response interpretation.
- LIME offers local explanations by showing how small input changes affect chatbot responses, providing focused insights on single interactions but lacking broader model coverage.
- SHAP applies game theory to reveal …