TL:DR

AI explainability (XAI) refers to a set of tools and frameworks that enable an understanding of how AI models make decisions, which is crucial to fostering trust and improving performance. By integrating XAI into the development process along with strong AI governance, developers can improve data accuracy, reduce security risks, and eliminate bias.

Introduction

As companies of all sizes and across industries increasingly deploy AI systems, these technologies integrate into critical applications. However, the rate of adoption is inconsistent. 

Here’s an example of why. A company might develop an AI solution using machine learning to power its manufacturing production, creating a safer and more efficient process. It’s an expensive proposition, so the company expects big things upon deployment. Yet, workers are hesitant to adopt it.

Why? Because more people are concerned than excited about the use of AI. Data privacy risks are the centre of this concern, as AI systems rely on large amounts of private data to operate. And the employees may not trust the AI models to keep them safe and make the right decisions.

That’s why it’s paramount for AI models to be trustworthy and transparent, which is at the core of the concept of explainable AI.

What Is Explainability in AI? 

AI explainability (XAI) refers to the techniques, principles, and processes used to understand how AI models and algorithms work so that end users can comprehend and trust the results. You can build powerful AI/ML tools, but if those using them don’t understand or trust them, you likely won’t get optimal value. Developers must also create AI explainability tools to solve this challenge when building applications.

Key Takeaways

• AI explainability is essential for building trust, ensuring regulatory compliance, and improving model performance.
• Techniques such as interpretable models, post-hoc explanation methods, and visualization tools can enhance AI explainability.
• Integrating explainability from the start, tailoring explanations for different stakeholders, and continuous monitoring are key best practices.

Understanding AI Explainability

AI models are complex. The inner workings are known to the developers but hidden from users. XAI helps users understand how models work and how they arrive at results.

The National Institute of Standards and Technology (NIST) proposed four key concepts for XAI:

1. Explanation: The system provides reasons for its outputs.
2. Meaningful: The explanations are understandable to the target audience.
3. Accuracy: The explanations correctly reflect the system's reasoning.
4. Knowledge limits: The system operates within its designed scope and confidence levels.

Focusing on these four principles can bring clarity to users, showcasing model explainability and inspiring trust in applications.

Types of AI Models

AI models are typically one of two types: 

1. Black box AI models: Black box AI models are highly complex, often using deep neural networks, which makes it challenging to understand decision-making. While they tend to deliver high accuracy, trusting the model’s reasoning is difficult since you can’t see inside. Diagnosing and correcting errors can also be challenging because root causes may not be obvious within the complexity.
2. White box AI models: By contrast, white-box AI models are clearer. You can see the logic and reasoning behind decisions. These AI solutions typically rely on simpler algorithms like decision trees or rules. These models may not achieve the same accuracy on complex tasks, but the inner workings are easier to understand.

Importance of AI Explainability

AI explainability aids in three key areas:

Building Trust

AI explainability creates a foundation of trust for users. This is especially important in mission-critical applications in high-stakes industries such as healthcare, finance, or areas that Google describes as YMYL (Your Money or Your Life).

Regulatory Compliance

Regulators are trying to catch up with the emergence of AI, and there are important decisions ahead about how and when laws and rules need to be applied. Regardless, explainable AI will be central to compliance to demonstrate transparency.

There are already some laws in place. For example, the EU’s General Data Protection Regulation (GDPR) grants individuals the “right to explanation” so that individuals can understand how automated decisions about them are made. This would apply in cases such as AI processes for loan approvals, resume filtering for job applicants, or fraud detection.

Improving AI Performance 

Besides explaining things to end users, XAI helps developers create and manage models. With a firm understanding of how AI makes decisions and outputs, developers are more likely to identify biases or flaws. This leads to better model tuning and improved performance.

Techniques for Enhancing AI Explainability

Developers can apply certain techniques to improve AI explainability.

Interpretable Models

AI interpretability is built into some AI models to make it easier to understand. These models follow a hierarchal structure of rules and conditions, such as:

• Decision trees: Represents the process as a tree structure, making it easy to follow the logic behind each decision
• Rule-based systems: Relies on a set of predefined rules to make decisions, providing inherent interpretability
• Linear regression models: Expresses the relationship between features and the output as a linear equation, allowing users to see how each feature influences the outcome

Post-Hoc Explanation Methods

For black-box models, explainability is more complex. Post-hoc explanation methods work by analysing the model’s input and outputs. Common AI explainability tools include:

• Local Interpretable Model-Agnostic Explanations (LIME): The LIME framework approximates the behaviour of a black-box model locally, providing explanations for individual predictions.
• SHapley Additive exPlanations (SHAP): The SHAP framework is based on game theory, assigning importance values to input features and quantifying their contributions to the model’s output.
• Attention Mechanisms: Used in natural language processing (NLP) and computer vision, attention mechanisms highlight the parts of the input that the model focuses on when making predictions.

Visualisation Tools

Visual representations can be helpful in explainability, especially for users who are not developers or data scientists. For example, visualising a decision tree or rules-based system using a diagram makes it easier to understand. It gives users a clear definition of the logic and pathways the algorithms choose to make decisions.

For image analysis or computer vision, a saliency map would highlight the regions in an image that contribute to an AI model's decisions. This could help machine operators better understand why algorithms position items in a specific way in production or reject parts for quality issues.

Developers can also create partial dependence plots (PDPs), which can visualise the impact of certain features on outputs. PDPs can show the non-linear relationships between input variables and model predictions.

Best Practices for AI Explainability

Following a few best practices can help ensure the successful integration of AI explainability.

Integrating Explainability from the Start

You can build your development roadmap by incorporating interpretability requirements during the design phase and documenting key system information at each step. This helps inform your explainability process and keeps models focused on accurate and unbiased data.

User-Centric Explanations

You will need explanations for deploying AI explainability for technical and non-technical users, so tailor your explanations accordingly. Data scientists will want to dive deeper into the inner workings than executives or line-level workers, who will be more focused on the practical implications of outputs.

Continuous Monitoring and Feedback

Explainability should be an ongoing process, especially for complex models that evolve over time as more data is gathered. As AI systems evolve new scenarios, explanations should be assessed and updated as necessary. 

User feedback will be crucial in the monitoring process to account for different scenarios or use cases to help improve the clarity of explanations and the accuracy of the AI model.

Challenges in Achieving AI Explainability

XAI has significant challenges that require addressing. The priority should be limiting bias. If the training data is biased, the model will make biased decisions. Follow AI governance to ensure data accuracy, security, and fairness. This is a crucial aspect of developing trustworthy AI.

Developers can overcome the issues with security and fairness by building in explainable AI principles from the start, highlighting the factors that influence decisions and showing how changing inputs change outputs. However, there’s often a trade-off between model accuracy and explainability, especially for models that rely on:

• High-dimensional data with large numbers of variables
• Reinforcement learning, where models learn through trial and error
• Generative Adversarial Networks (GANs) which use two neural networks to help improve outcomes.

In some cases, the best approach is combining AI with human oversight. Such human-in-the-loop systems empower people to leverage AI while maintaining control over the final decision-making process.

Final Thoughts

AI explainability is an essential part of responsible AI development. By making the decision-making process transparent and understandable, you can establish a higher level of trust and comfort among users. This also aids in ensuring regulatory compliance and improving system performance.

As AI integration continues across industries and various aspects of our lives, organizations must prioritize AI explainability in their development strategies. This helps inspire confidence in outcomes and promotes a culture of transparency and accountability within development teams.

The pursuit of XAI is a key component in AI governance and ethical use. As AI systems evolve and become more powerful — and more complex — ensuring this transparency is increasingly crucial to mitigate potential risks and adhere to ethical principles.

Zendata integrates privacy by design across the entire data lifecycle, emphasizing context and risks in data usage. We help companies with insights into data use, third-party risks and alignment with data regulations and policies. 

If you want to learn more about how Zendata can help you with AI governance and compliance to reduce operational risks and inspire trust in users, contact us today.

FAQ

What role does feature importance play in AI explainability?

In AI explainability, understanding feature importance is crucial. It helps clarify which inputs in a dataset most significantly impact the model’s predictions. Techniques like SHAP (SHapley Additive exPlanations) quantify the influence of each feature, providing insights into model behavior and helping identify biases in the data or model.

How do conditional expectations contribute to model interpretability?

Conditional expectations are used in AI models to predict the expected outcome based on specific input conditions. This method is particularly useful in interpretable models like linear models, where it clarifies how different features are weighted, enhancing transparency about the decision-making process.

Can you explain the difference between a black box model and an interpretable model in AI systems? 

Black box models, like deep neural networks, are complex and their internal workings are not readily accessible, making it difficult to understand how decisions are made. In contrast, interpretable models, such as decision trees, offer clear insights into the decision-making process, as their structure allows users to see the exact path taken to reach a conclusion.

What is the significance of agnostic tools in enhancing AI interpretability?

Agnostic tools in AI, such as LIME (Local Interpretable Model-Agnostic Explanations), are designed to work with any AI model, providing flexibility in generating explanations. These tools help in understanding black box models by approximating how changes in input affect predictions, which is vital for improving transparency across various AI systems.

How does the concept of permute feature importance assist in understanding AI models?

Permuting feature importance involves randomly altering the values of one feature at a time in a dataset to observe how these changes affect the model’s accuracy. This technique helps identify which features are most predictive and is an effective method for assessing the robustness and reliance of AI models on specific data inputs.

In what way do decision trees support explainable artificial intelligence? 

Decision trees support explainable artificial intelligence by visually representing decisions and their possible consequences. This format allows both technical and non-technical stakeholders to trace the logic behind each decision, making it easier to understand and trust the AI system’s outputs.

What challenges do classifiers face in maintaining fairness in AI models? 

Classifiers in AI models can inadvertently propagate bias if the training data contains biased examples or if the features selected for making predictions carry implicit biases. Addressing these challenges involves using techniques that monitor and adjust the classifier's behavior to ensure decisions are fair and unbiased.

How do deep neural networks complicate the explainability of AI systems? 

Deep neural networks, comprising many layers and non-linear relationships, complicate explainability due to their opaque structure. Their complexity can obscure the rationale behind specific outputs which makes it difficult to diagnose errors or understand decision pathways.

What is the role of a linear model in promoting AI explainability? 

Linear models promote AI explainability by illustrating a direct, understandable relationship between inputs and outputs. They allow stakeholders to see how each feature influences the prediction, thus providing a straightforward and transparent view of the model’s functioning.

How do machine learning models handle unexpected predictions, and what does this mean for AI explainability? 

Machine learning models handle unexpected predictions by using techniques such as anomaly detection to flag unusual outputs. This aspect of AI explainability is crucial for maintaining trust and reliability, as it ensures that the AI system can identify and react to potential errors or outlier data effectively.

‍