Explainable AI - building trust through insights into the operation of AI models

Introduction to Explainable AI (XAI)

In today's era, where Artificial Intelligence (AI) is increasingly employed in decision-making processes that influence our daily lives, a central question arises: How can we trust and comprehend the decisions made by AI models? When an AI model is based on a simple statistical algorithm, it is usually straightforward to understand how the results are derived. However, the scenario is different for complex models like Deep Neural Networks (DNNs). The structure and decisions of these models are often not readily understandable. Explainable AI (XAI) addresses this challenge. But how precisely can XAI unveil the "Black Box" nature of such models?

Explainable AI in a Nutshell

• Explainable AI (XAI) aims to render the decision-making processes and functionalities of AI models transparent and comprehensible.
• It empowers users and developers to trace the logic behind the predictions and decisions made by AI, which is crucial for building trust and fostering the acceptance of these technologies.
• XAI supports developers in rationalizing model operations through meaningful and accessible visualizations, contributing to the enhancement of AI models.
• End users gain confidence as the workings and decision-making of complex AI models become understandable.

XAI - Insights into the AI Model

To understand the architecture of a specific AI model, there are methods and techniques aimed at making the internal mechanisms and functionalities of AI models transparent and comprehensible. This approach is crucial to unveil the complexity and the so-called "Black Box" nature of modern AI systems, especially Deep Neural Networks. This insight is vital for creating transparency and strengthening trust in the decisions made by AI systems. Particularly in critical use cases, such as healthcare or finance, understanding how a model arrives at its conclusions is essential.

Surrogate models are one way to approximate the functioning of an underlying AI model. Surrogate models are designed to be more easily understood and interpreted, without sacrificing essential information about the decision-making of the original model. The following illustration depicts a surrogate model in the form of a Decision Tree using the example of the AltaSigma AI Platform.

Another important element is the consideration of Feature Importance. This aspect delves into how individual features or input data strongly influence the results of the AI model. Analyzing feature importance allows us to identify which factors are most crucial for the model and how they impact predictions or decisions.

XAI - Insights into decision-making

To make the decision-making processes of AI models comprehensible, it is crucial to understand both the internal structure and the specific mechanisms of decision formation. Various methodological approaches are available for this purpose.

SHAP (SHapley Additive exPlanations), based on game-theoretical principles, explains the output of any Machine Learning model. The underlying Shapley values were originally introduced in 1951 by American economist Lloyd Shapley for fair profit distribution in cooperative games. In the context of Machine Learning, Shapley values represent the relative contribution of each feature (considered as a "player") to the overall performance of the model (the "game"). The central idea of SHAP is to assess the influence of a feature on the prediction by comparing how the model's prediction looks both with and without that particular feature. This difference, resulting from the presence or absence of a feature, is termed the Shapley value. Shapley values thus provide a quantitative assessment of the impact of each individual feature on the model's prediction. Through this approach, SHAP enables a deeper understanding of how various input variables collectively contribute to the performance of an AI model, significantly enhancing transparency and interpretability in the realm of machine learning.

LIME (Local Interpretable Model-agnostic Explanations) is model-agnostic like SHAP but follows a different approach. In terms of functionality, it first slightly modifies the input example for which an explanation is to be generated to create a set of similar data points. It then observes how the model's predictions change for these modified data points. Based on these observations, it creates a simple, interpretable model (like a linear model) that is locally valid around the input example. For text-based inputs, these modifications may involve adding or removing words.

Reason Codes are special codes or brief explanations that elucidate the reasons for a specific prediction or decision of an AI model. They aim to make the outputs of AI systems more understandable for end users. For instance, in the finance sector, Reason Codes are used to assess loans and risks. In this context, they are also referred to as Credit Score Risk Factors or Adverse Action Codes. These numerical or alphanumeric codes are linked to various credit rating factors and are accompanied by short descriptions explaining what has the most significant impact on this score. Reason Codes are listed in a sorted manner, with the factor having the greatest influence ranked first. They are an essential component of responsible AI model management, playing a crucial role in establishing a framework for ethical and fair AI. They help identify and minimize biases and hallucinations in AI models by making the influence of discriminatory factors such as gender or age on model predictions transparent. This not only improves the accuracy of AI models but also strengthens users' trust in this technology.

XAI - Challenges and Perspectives for the Future

The primary challenge of Explainable AI (XAI) lies in navigating the tension between complex and highly precise AI models on one side and achieving a straightforward understanding of the model output on the other. Ethical considerations are gaining increasing importance as XAI is deployed in sensitive areas such as medicine and finance, where fair and unbiased decisions are crucial. In the future, XAI will be indispensable for fostering trust in AI decisions and ensuring their societal acceptance. The focus is on making AI decisions transparent, comprehensible, and ethically responsible.