A Beginner’s Guide to Explainable AI and Its Role in Building Trust in AI Systems

Key takeaways

Explainable AI provides clear explanations for how it arrived at an outcome or why it made a decision, helping build trust in AI systems. Here are some important things to know:

• The explainable AI market is experiencing rapid growth, expected to reach $21.06 billion by 2030 [1].

• Explainable AI is key to developing responsible AI systems, improving decision-making, reducing bias in data, and enhancing AI adoption rates.

• You can use explainable AI for various industry applications, like preventative treatment in health care and fraud detection in financial services.

Explore the fundamentals of explainable artificial intelligence (AI), including the principles it’s based on, common methods for building explainability in AI systems, and real-world applications of explainable AI. If you’re ready to start building expertise in AI, enroll in the Google AI Essentials Specialization. You’ll learn how to use AI tools for informed decision-making, as well as how to use AI responsibly by reducing bias. 

What is explainable AI?

Explainable AI describes the processes that allow you to understand why a machine learning algorithm made a certain decision. Explainable AI models are crucial for building trust in AI’s predictions and driving the development of responsible AI.

Several machine learning models, such as neural networks and deep learning algorithms, are difficult to interpret, often referred to as “black boxes.” Since these models are produced directly from data, even the model designers can’t explain how they arrived at a result. This can lead to lowered trust among customers and other stakeholders if they can’t understand the reason behind a decision that impacts them. By exposing the underlying decision-making mechanisms of the machine learning system, explainable AI fosters end-user trust, ensures compliance with regulatory standards, and promotes the implementation of responsible AI systems built on transparency and accountability.

This demand for transparency is driving explainable AI adoption worldwide. According to Grand View Research, the explainable AI market will reach $21.06 billion by 2030, with IT, health care, and banking and financial services emerging as high-use sectors for explainable AI in 2024 [1]. 

Explainability vs. interpretability

Explainability refers to the ability to explain how an AI model arrived at an outcome in a manner that is understandable to nontechnical individuals. It involves using certain methods to describe the AI’s decision-making behavior to obtain information on which features or inputs drove it to make a particular prediction. Explainability is especially important for building end-users’ trust in the AI system. 

Interpretability, meanwhile, is the extent to which you can understand the internal workings of the AI model when making decisions. Interpretability is important for building highly transparent AI systems, where it’s necessary to understand exactly how a model arrived at a result. This makes interpretability an important consideration for model developers to ensure the AI system is functioning according to their expectations.

What is the significance of explainability in trusted AI systems?

Explainability is crucial in trusted AI systems as it allows you to understand exactly how and why the model makes decisions. Without transparency in decision-making, complex AI models can function like black boxes, leading to mistrust among end users and stakeholders. Trust is even more important in high-stakes fields like health care and finance, where explainability ensures AI decisions comply with relevant regulations while maintaining accuracy. Embedding AI systems with explainability, fairness, and ethics is key to implementing responsible AI practices and fostering user trust in automated systems, which, in turn, can help in quicker adoption of AI technologies.

Principles of explainable AI

According to the National Institute of Standards and Technology (NIST), explainable AI systems must adhere to the following four principles [2]:

• Explanation: The AI system’s output must be accompanied by evidence.

• Meaningful: Each user must be able to understand the AI system’s explanations.

• Accuracy: The AI system’s output production process must be accurately reflected in the explanation.

• Knowledge limitations: The AI system’s function must not extend beyond its designed parameters or confidence level in its output.

Explainable AI methods

While the explainable AI techniques you use will mainly depend on your model and target end user, some common techniques used to explain AI models include intrinsically interpretable methods, feature importance, model-based methods, counterfactual explanations, and visualization.

Intrinsically interpretable methods

Algorithms like decision trees and linear regression are inherently transparent by design. For example, decision trees can clearly display which inputs led to the outcome through a branching decision-making structure. Although these models are highly interpretable, their reduced complexity makes them unsuitable for nonlinear tasks such as image recognition.

Feature importance

Feature importance highlights which model features or input variables had the most impact on a model’s decision, allowing you to better understand the model’s decision-making framework. For example, the permutation importance method evaluates the effect of the random shuffling of feature parameters on the model’s performance.

Model-based methods

You can categorize model-based methods for AI explainability into model-specific and model-agnostic methods.

Model-specific methods are specific to a certain type of model and provide explanations based on that model’s distinct characteristics. You can use them for simple models, like tree interpreters for random forest algorithms, as well as more advanced models, like the Deep Learning Important FeaTures (DeepLIFT) method for deep neural networks.

In contrast, you can apply model-agnostic methods to any type of machine learning model, irrespective of its type. Some popular model-agnostic methods are SHapley Additive exPlanations (SHAP), which applies game theory and Shapley values to determine how each feature contributes to the outcome, and Local Interpretable Model-agnostic Explanations (LIME), which creates a simple model to mimic the AI’s behavior for a specific prediction.

Counterfactual explanations

Counterfactual explanations consider what model features would have to change for the AI system to generate different outcomes. This might involve using methods like gradient-based optimization for neural networks or tools like DiCE (Diverse Counterfactual Explanations), a prebuilt Python library that can simplify explanation generation.

Visualization

Visualization tools employ charts, graphs, data analytics metrics, and dashboards to clearly illustrate how a model arrives at an outcome. Some popular tools include heatmaps, TensorBoard, saliency maps, and the Captum framework for PyTorch.

Read more: Data Visualization: Definition, Benefits, and Examples

Why is explainability important in AI? Pros and cons to consider

Explainable AI helps build trust among individuals and identify issues more easily. Some other key benefits of explainable AI systems include the following: 

• Promotes better decision-making by providing transparent and clear reasons for decisions

• Enhances AI optimization by allowing you to monitor model performance, key drivers, and accuracy metrics, an aspect unavailable in black-box models

• Helps reduce bias in your AI model by exposing patterns it detected in your data, allowing your team to mitigate any such bias or errors

• Allows for increased AI adoption by fostering trust and transparency in AI and machine learning systems

• Ensures regulatory compliance by allowing you to audit the reasoning process of the model

Despite its advantages, explainable AI does have certain limitations:

• It may be computationally extensive, limiting scalability across real-world applications.

• Transparency and explainability might lower the performance of explainable AI models compared to black box models.

• It can risk the exposure of confidential data in explanations, leading to privacy concerns.

Real-world applications of explainable AI

You’ll find applications and use cases for explainable AI across several industries:

Health care

In medical imaging, explainable AI can help physicians understand why it flagged a patient’s X-ray and identify minute anomalies unobservable to the human eye, improving diagnosis and treatment decision-making. Additionally, these AI systems can predict a patient’s likelihood of developing a disease and explain why it’s likely, allowing the formulation of preventative measures. For example, a 2020 study employed an explainable AI early warning system to predict the likelihood of developing acute critical illnesses based on electronic health record data [3]. Explainable AI systems can also predict a patient’s response to a drug, helping physicians make personalized treatment plans.

Banking and financial services

Explainable AI can help with detecting financial fraud by sending out notifications when suspicious activities trigger its alert system, offering clear explanations of what caused the alert. Additionally, explainable AI can make loan approval processes more transparent by explaining the reasons behind loan approval or denial. This can help financial institutions identify biases in their data and adhere to fair lending policies. For example, financial services company Wells Fargo employs adverse effect checks in its AI models to uncover biases in training data and promote responsible AI practices [4].

Autonomous vehicles

Explainable AI in self-driving vehicles provides detailed descriptions to explain why the vehicle took a certain action, such as abruptly braking to avoid an obstacle, helping build trust and reliability, particularly for decisions that involve safety. For example, a 2023 study employed explainable deep learning models in autonomous vehicles to generate meaningful textual explanations for driving scenarios, helping improve end-user trust [5].

Military and defense

Explainable AI can help military personnel develop trust in the autonomous weapon systems and other AI-based combat applications used in defense operations. The US Department of Defense’s Defense Advanced Research Projects Agency (DARPA) is developing an explainable AI program that aims to create machine learning models that balance transparency with prediction accuracy, promoting user trust and the effective management of AI [6].

Is ChatGPT an explainable AI?

No, ChatGPT is a non-explainable AI, as the generative pre-trained transformer (GPT) model underlying ChatGPT uses a highly complex internal structure and multiple parameters to achieve high performance, making it a “black box” in terms of transparency. 

Getting started in explainable AI

If you want to start building your skills in explainable AI, the first step is mastering the core principles of AI and explainability. Consider taking an online course like the Google AI Essentials Specialization or reading about explainable AI terminology on platforms like GitHub. Learn about specific explainable AI methods like LIME and SHAP by looking up official documentation online. You can also explore explainable AI features and algorithms by looking into tools like Google’s Vertex Explainable AI and IBM’s AI Explainability 360. Once you have a clear understanding of AI and explainability, you can start implementing explainable AI in your workflow. 

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