Responsible AI: Principles and Practices/

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Explainability Techniques for Black Box Models

Explainability Techniques for Black Box Models

Learn about the different types of model explainability techniques and where to apply them.

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Imagine entrusting critical decisions, such as medical diagnoses, loan approvals, or autonomous vehicle operations, to an AI system that provides answers without offering any insight into how those conclusions were reached. This enigmatic behavior raises serious questions about accountability, transparency, and the ethical implications of AI-driven decision-making.

In this lesson, we’ll delve into the challenges posed by black box AI systems, understand the need for diverse explainability approaches, and study some of the popular XAI approaches used in the pursuit of responsible and ethical AI-driven solutions.

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Black box AI challenges
Black box AI challenges

The need for different types of Explainable AI (XAI) approaches arises from the diverse requirements, complexities, and characteristics of various machine learning models, applications, and stakeholders.

Having a range of XAI methods allows practitioners to choose the most suitable approach for a given context, promoting transparency, trust, and the responsible use of AI.

Below, we study some of the key reasons why different types of XAI approaches are essential:

Need for Diverse Explainability Approaches

Factors

Requirements



Model diversity

  • Different machine learning models have varying structures and complexities.


  • XAI methods must adapt to these differences to provide meaningful explanations for different model types.



Application variety

  • AI is applied to a wide range of domains.


  • Each domain may require explanations tailored to its specific context and user needs.



Stakeholder preferences

  • Various stakeholders, such as data scientists, business analysts, regulators, etc., have distinct requirements for model explanations.


  • Some may need detailed, technical insights, while others require high-level, intuitive explanations.





Interpretability levels

  • Different XAI methods cater to varying interpretability levels.


  • Some situations call for local interpretability, where explanations focus on individual predictions.


  • Others require global interpretability, providing insights into overall model behavior.


Data types

  • Different types of data, such as images, text, tabular data, and time series, require specialized XAI techniques.

Global model-agnostic methods

Global interpretation methods provide insights into the overall behavior of a machine learning model, and they are complemented by local methods. Global methods often involve calculating expected values based on the data distribution.

We will delve into the following model-agnostic global interpretation techniques:

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