Explainable AI (XAI)

What is Explainable AI?

Explainable AI, or XAI, is a collection of procedures and tools used to help and support machine learning algorithms and the overall ML modelling. It aims to produce a trustworthy understanding of the models’ predictions and outputs. With the rapid advancement of the artificial intelligence field, the processes and operations behind machine learning modelling becomes more complex and harder to be comprehended. Unable to understand a ML model refers to a “black box”.

The use of XAI helps organisations, businesses, and researchers recognise the expected impact as well as potential issues from a machine leering model. it also helps describe the model, by looking at its accuracy, transparency, , behaviour, fairness and bias, and improvement.

There are several benefits of implementing XAI. The use of such methods can support model monitoring, analysis, and further developmental improvement. Explainable AI can assure developers and companies whether the system works properly and efficiently. Organisations within the industry may also have to follow regulations and standards.

Types of Explainable AI

Global explainability refers to understanding the overall behavior of the model. It answers questions like: “What features does the model generally consider important?” or “How does the model make predictions for a wide variety of inputs?”

Example: Understanding that a decision tree or linear regression model gives more weight to certain features when making predictions.

Local explainability refers to understanding how the model made a particular decision for a specific input. It answers questions like: “Why did the model make this decision for this specific instance?”

Example: In a loan application, understanding why the AI approved or rejected a specific applicant.

Challenges and Limitations of Explainable AI (XAI)

Trade-off Between Accuracy and Explainability

Simpler models like decision trees or linear models are more explainable. But they may have lower accuracy compared to complex models like deep neural networks or ensemble models. XAI tries to balance the trade-off between interpretability and performance.

Complex Models

Deep learning models, which are inherently complex, have millions of parameters and layers. As such, making them difficult to interpret even with XAI techniques.

Subjective Nature of Explanations

What qualifies as a good explanation can vary depending on the user’s background (e.g., a data scientist vs. a layperson). This makes it difficult to create universally understandable explanations.

Data and Feature Engineering

If the input data or feature engineering is flawed, explanations based on those inputs can also be misleading or biased.

Examples of XAI

In Healthcare, doctors can use XAI to understand why an AI system recommends a particular diagnosis or treatment. This can lead to better trust and collaboration between AI systems and medical professionals.

Within Finance, especially in banking and insurance, XAI can help explain why an individual was approved or denied for a loan or why a claim was flagged as fraudulent. As such, ensuring compliance with regulations.

In the Legal and Judicial Systems, AI models can be used to predict recidivism rates. But these systems must be interpretable to ensure fair treatment and avoid perpetuating bias.

Within Autonomous Vehicles, explainable AI (XAI) helps clarify how self-driving cars make decisions. Such as why they stopped or changed lanes. This is critical for debugging and safety purposes.

Next: Ethical AI