Explainable AI

by | Aug 1, 2024 | Uncategorized |

 

Explainable AI

 

This article delves into the concept of Explainable AI (XAI), a set of techniques and methods designed to make the outputs of machine learning models understandable and reliable for human users. We will explore why explainability is essential, categorize explainability techniques into global and local approaches, and provide an overview of key XAI methods like LIME, SHAP, ELI5, Partial Dependence Plots, and Accumulated Local Effects. Additionally, we will examine the architecture of XAI, which includes the machine learning model, explanation algorithm, and interface, and discuss the advantages and limitations of implementing XAI.

Explainable artificial intelligence (XAI) refers to a collection of procedures and techniques that enable machine learning algorithms to produce output and results that are understandable and reliable for human users. The need for explainable AI arises from the fact that traditional machine learning models are often difficult to understand and interpret. These models are typically black boxes that make predictions based on input data but do not provide any insight into the reasoning behind their predictions.

The explainability techniques are mainly divided into two categories:

  • Global: they explain the model in general, noting its generic operating rules.
  • Local: They explain for every single data, how the model reasoned and the rules that led to a certain output.

Techniques:

  1. LIME (Local Interpretable Model-agnostic Explanations): LIME is a popular XAI approach that uses a local approximation of the model to provide interpretable and explainable insights into the factors that are most relevant and influential in the model’s predictions. To implement LIME in python, you can use the lime package, which provides a range of tools and functions for generating and interpreting LIME explanations.
  2. SHAP (SHapley Additive exPlanations): SHAP is an XAI approach that uses the Shapley value from game theory to provide interpretable and explainable insights into the factors that are most relevant and influential in the model’s predictions. To implement SHAP in python, you can use the shap package, which provides a range of tools and functions for generating and interpreting SHAP explanations.

  1. ELI5 (Explain Like I’m 5): ELI5 is an XAI approach that provides interpretable and explainable insights into the factors that are most relevant and influential in the model’s predictions, using a simple and intuitive language that can be understood by non-experts. To implement ELI5 in python, you can use the eli5 package, which provides a range of tools and functions for generating and interpreting ELI5 explanations.
  2. Partial Dependence Plot: The partial dependence plot (short PDP or PD plot) shows the marginal effect one or two features have on the predicted outcome of a machine learning model. A partial dependence plot can show whether the relationship between the target and a feature is linear, monotonic or more complex. For a perturbation-based interpretability method, it is relatively quick. PDP assumes independence between the features and can be misleading interpretability-wise when this is not met.
  3. Accumulated Local Effects (ALE): Accumulated Local Effects (ALE) is a method for computing feature effects. The algorithm provides model-agnostic (black box) global explanations for classification and regression models on tabular data. ALE addresses some key shortcomings of Partial Dependence Plots (PDP).

Explainable AI (XAI) architecture consists of three main components:

  1. Machine Learning Model: The core component that uses algorithms and techniques (like supervised, unsupervised, or reinforcement learning) to make predictions from data across various applications such as medical imaging and natural language processing.
  2. Explanation Algorithm: This component provides insights into the factors influencing the model’s predictions. It employs approaches like feature importance, attribution, and visualization to elucidate the model’s workings.
  3. Interface: This component presents the insights generated by the explanation algorithm to users. It leverages technologies such as web applications and visualizations to offer an intuitive and user-friendly way to access and interact with the information.

These components work together to enhance the transparency, interpretability, and trustworthiness of machine learning models across different domains​

Advantages:

  1. Makes AI more trustworthy.
  2. Provides insight against adversarial attacks.
  3. Improved decision-making.
  4. Reduced risks and liabilities.

Limitations:

  1. Oversimplification.
  2. Limited scope and domain-specificity.
  3. Lack of standardization and interoperability.

Explainable AI (XAI) bridges the gap between complex machine learning models and human understanding, enhancing transparency, interpretability, and trustworthiness. By leveraging techniques such as LIME, SHAP, and ELI5, and understanding the architecture of XAI systems, stakeholders can gain valuable insights into AI decision-making processes. Despite its challenges, including potential oversimplification and domain-specific limitations, XAI plays a crucial role in improving decision-making, mitigating risks, and fostering trust in AI applications across various domains.