Unraveling Scenario-Based Behavior of a Self-Learning Function with User Interaction

Abstract

In recent years, the field of Artificial Intelligence (AI) and Machine Learning (ML) has witnessed remarkable advancements, revolutionizing various industries and domains. The proliferation of data availability, computational power, and algorithmic innovations has propelled the development of highly sophisticated AI models, particularly in the realm of Deep Learning (DL). These DL models have demonstrated unprecedented levels of accuracy and performance across a wide range of tasks, including image recognition, natural language processing, and complex decision-making. However, amidst these impressive achievements, a critical challenge has emerged - the lack of interpretability.Highly accurate AI models, including DL models, are often referred to as black boxes because their internal workings and decision-making processes are not readily understandable to humans. While these models excel in generating accurate predictions or classifications, they do not provide clear explanations for their reasoning, leaving users and stakeholders in the dark about how and why specific decisions are made. This lack of interpretability raises concerns and limits the trust that humans can place in these models, particularly in safety-critical or high-stakes applications where accountability, transparency, and understanding are paramount.To address the challenge of interpretability, Explainable AI (xAI) has emerged as a multidisciplinary field that aims to bridge the gap in understanding between machines and humans. xAI encompasses a collection of methods and techniques designed to shed light on the decision-making processes of AI models, making their outputs more transparent, interpretable, and comprehensible to human users.The main objective of this paper is to enhance the explainability of AI-based systems that involve user interaction by employing various xAI methods. The proposed approach revolves around a comprehensive ML workflow, beginning with the utilization of real-world data to train a machine learning model that learns the behavior of a simulated driver. The training process encompasses a diverse range of real-world driving scenarios, ensuring that the model captures the intricacies and nuances of different driving situations. This training data serves as the foundation for the subsequent phases of the workflow, where the model's predictive performance is evaluated.Following the training and testing phases, the predictions generated by the ML model are subjected to explanation using different xAI methods, such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations). These xAI methods operate at both the global and local levels, providing distinct perspectives on the model's decision-making process. Global explanations offer insights into the overall behavior of the ML model, enabling a broader understanding of the patterns, relationships, and features that the model deems significant across different instances. These global explanations contribute to a deeper comprehension of the decision-making process employed by the model, allowing users to gain insights into the underlying factors driving its predictions.In contrast, local explanations offer detailed insights into specific instances or predictions made by the model. By analyzing these local explanations, users can better understand why the model made a particular prediction in a given case. This granular analysis facilitates the identification of potential weaknesses, biases, or areas for improvement in the model's performance. By pinpointing the specific features or factors that contribute to the model's decision in individual instances, local explanations offer valuable insights for refining the model and enhancing its accuracy and reliability.In conclusion, the lack of explainability in AI models, particularly in the realm of DL, presents a significant challenge that hinders trust and understanding between machines and humans. Explainable AI (xAI) has emerged as a vital field of research and practice, aiming to address this challenge by providing methods and techniques to enhance the interpretability and transparency of AI models. This paper focuses on enhancing the explainability of AI-based systems involving user interaction by employing various xAI methods. The proposed ML workflow, coupled with global and local explanations, offers valuable insights into the decision-making processes of the model. By unraveling the scenario-based behavior of a self-learning function with user interaction, this paper aims to contribute to the understanding and interpretability of AI-based systems. The insights gained from this research can pave the way for enhanced user trust, improved model performance, and further advancements in the field of explainable AI.