Unraveling Scenario-Based Behavior of a Self-Learning Function with User
Interaction
Authors: Stang Marco , Schindewolf Marc , Sax Eric 
Published: July 2023

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.