Plausible Explanation

Plausible explanation in AI focuses on generating understandable and trustworthy justifications for model predictions, aiming to bridge the gap between complex algorithms and human comprehension. Current research emphasizes diverse explanation methods, including counterfactual examples, feature attributions (often aggregated using optimization techniques), and natural language explanations generated via large language models (LLMs) or other architectures like variational autoencoders. This field is crucial for building trust in AI systems, particularly in high-stakes domains like medicine, and for improving model transparency and accountability by identifying and mitigating biases or spurious correlations.