AI Machine Interpretability and Explainable AI: Knowing Why and How AI Makes Its Decisions

AI systems are often viewed as ‘black box’ systems with internal processes that cannot be logically identified and understood. This can be a problem when a flood of incoming natural language data overwhelms manual processing. In these cases, an AI system is used to review and apply criteria to understand and evaluate the data.

For example, the incoming data may be a large volume of applications for a specific job, a flow of input from a company’s customers, or patient feedback about adverse events associated with a new drug. These types of situations cause problems that AI explainability is intended to solve.

AI systems process and prioritize the data before any human views it. Also, AI systems are being used as the decision-makers in high-risk situations for individuals, even though the quantity of data could be manually processed. For example, an AI system may suggest the appropriate medical treatment in healthcare or whether to approve (or reject) a loan application in banking.

This post discusses AI explainability, no matter what it is called. The first section clarifies the meanings of Interpretability in Machine Learning and Explainable AI. The following sections describe why ML explainability matters, its impacts on people, and its challenges.

Then there is a section on how regulators around the world are promoting frameworks to regulate AI and force AI explainability. Finally, the section before the Conclusion contains links to articles that provide more details for those exploring or implementing explainable machine learning.

What is Explainable AI?

AI researchers and regulators typically describe understanding how an AI system makes decisions with very different words. Researchers talk about AI interpretability, and regulators describe it as explainable AI. The two terms are closely related, but there are subtle differences in their scope and focus.

This post uses the term AI Explainability to include both machine interpretability and XAI (explainable AI). AI Explainability fosters trust and accountability in AI systems, helps identify and mitigate biases, and allows for better human oversight and control.

Explanations allow developers to correct biases and erroneous factors in decision-making as they become known. Explanations are also crucially important for tasks such as diagnosing diseases or making loan approvals, where transparency and justifying decisions are essential.

Machine Interpretability

The broad concept of interpretable machine learning refers to the ability to understand the inner workings of any machine learning model, not just AI models. Model interpretability emphasizes understanding how the model arrives at its predictions by analyzing the AI’s internal mechanisms and relationships between variables. The models being explained are typically simpler models with a clear structure and logic.

Use: Machine Interpretability tends to be discussed by developers or data scientists who want to understand the intricacies of the model for debugging AI, optimization, or further research purposes.

Examples: A linear regression model is an example of a machine-interpretable model. By examining the coefficients associated with each input feature, you can understand how they contribute to the final prediction. Other examples include decision trees, other types of regression models, and rule-based systems.

Explainable AI (XAI)

Explainable Artificial Intelligence (XAI) has a broader scope. XAI encompasses various techniques used to explain the decision-making processes of any AI model, even complex ones that might not be inherently interpretable.

For complex models, XAI techniques might only provide insights into the factors most influencing the model's prediction. The crucial issue in model explainability is whether it provides the information that enables a human to decide if the decision is appropriate and acceptable for the situation.

Use: XAI techniques can be applied to explain the behavior of simple and complex models, including deep learning models with intricate structures.

Examples: Techniques like feature importance, attention mechanisms, and counterfactual explanations fall under the umbrella of XAI. An XAI tool used for fraud detection in financial transactions could highlight the red flags identified in a suspicious transaction. If asked, an XAI system might explain to the driver of a self-driving car why it made a particular maneuver, such as changing lanes or applying the brakes.

Why AI Explainability Matters

Trust and Transparency: Many AI models, especially complex ones using deep learning, can be like black boxes. Users see the input and the output, but the reasoning behind the decision remains unclear. XAI informs the users how these models arrive at decisions, fostering trust and confidence in their results.
Fairness and Bias Mitigation: AI models can perpetuate existing societal biases if trained on biased data. Explainable artificial intelligence allows us to identify and address these biases within the model, promoting fairer outcomes in areas like loan approvals, hiring algorithms, and facial recognition systems. This leads to fairer and more ethical AI applications.
Human Oversight and Control: Understanding an AI model's decision-making process allows humans to identify potential flaws and unexpected behaviors. This enables us to maintain control over AI systems and ensure they function as intended.
Human-AI Collaboration: As AI takes on more complex tasks, effective collaboration with humans is crucial. XAI can facilitate this collaboration by enabling humans to understand and contribute to the decision-making process.
Debugging and Improvement: By understanding how an AI model reaches a specific decision, developers can identify potential issues or areas for improvement. This can lead to more robust and reliable AI systems.

AI Explainability’s Impact on People

Increased User Confidence: When people understand how AI systems work, they're more likely to trust their recommendations and outputs. This is crucial for applications in healthcare, finance, autonomous vehicles, and other risk-of-harm situations.
Empowerment and Control: Explainable AI empowers people to understand how AI systems are impacting their lives. This can lead to greater transparency and accountability in areas where AI is used in decision-making processes. By understanding how AI systems work, people have a greater sense of control over their interactions with them.
Reduced Bias: By making AI decision-making processes more transparent, XAI can help identify and mitigate biases in areas like loan approvals, facial recognition software, or hiring algorithms. Without understanding how these decisions are made, individuals can be unfairly impacted by biased or erroneous outcomes.
Improved User Experience: If people understand the reasoning behind AI recommendations (e.g., product suggestions on an e-commerce platform), they can make more informed choices. XAI can lead to a more transparent, user-friendly experience.
Improved Human-AI Collaboration: By understanding each other's strengths and limitations, humans and AI can work together more effectively. Humans can provide high-level goals and ethical considerations, while AI handles complex data analysis and pattern recognition.
Job Creation: The field of XAI is creating new job opportunities for researchers, developers, and ethicists who specialize in making AI models explainable and addressing potential biases.

Challenges and Considerations of Explainable AI Principles

Balancing Explainability and Accuracy: Sometimes, creating highly accurate models can come at the cost of explainability in AI. Researchers are working on finding a balance between the two.
Complexity of XAI Techniques: Some XAI techniques themselves can be complex, requiring specialized knowledge to understand. Explanations need to be tailored to the audience's level of technical knowledge. Explanations that are too technical might be confusing, while overly simplified explanations could lack detail.
The Human Element: Even with XAI, human interpretation and oversight remain crucial. XAI doesn't eliminate the need for human judgment and ethical considerations when deploying AI systems.
Standardization: There's a lack of standardized methods for XAI, making it difficult to compare and evaluate the explainability of different AI models.

How Regulators Promote XAI

Regulatory bodies around the world are developing frameworks that outline best practices for XAI development and deployment. Examples include the European Union's General Data Protection Regulation (GDPR) and the "Algorithmic Accountability Act" proposed in the US.

Regulatory Frameworks: Several countries and regions are developing regulatory frameworks that mandate a certain level of explainability in machine learning systems used in high-risk applications. For example, the European Union's General Data Protection Regulation (GDPR) includes provisions for "meaningful information about the logic involved" in automated decision-making.
Industry Standards: Collaboration between regulators, industry leaders, and academia can lead to the development of standardized methods for XAI. These standards would provide a framework for developers to build explainable AI models and establish benchmarks for assessing explainability.
Public Awareness Campaigns: Regulatory bodies can raise public awareness about the importance of XAI and empower individuals to question and understand the AI systems they encounter in their daily lives.

Details About Implementation

These links provide more details about implementing AI explainability and interpretability of AI, especially for those who are new to the topic. The articles are not behind paywalls (at the time of publication).

Conclusion

Whether described as Machine Interpretability or Explainable AI, the focus on AI explainability has the potential to revolutionize the way we interact with AI. It can increase both responsible development and collaboration with users. As the field continues to evolve, we can expect to see even more innovative and user-friendly approaches to explaining how AI makes the decisions that impact our lives.

However, it is important to understand the differences between interpretation and explanation. As an analogy, imagine a complex machine like a car engine.

Machine Interpretability: This would be like understanding the internal mechanics of the engine, how different parts interact, and the physical principles behind its operation. It might involve analyzing diagrams, specifications, and the relationships between gears, pistons, and other components.
Explainable AI: This would be like explaining how the car engine works to someone who doesn't know much about mechanics. You might use simpler terms, analogies, or visualizations to convey the basic idea of how the engine converts fuel into power to move the car. The crucial issue is explaining enough details so that the user can decide whether to agree or disagree with the decision and the logic used to support it.

In essence:

Machine interpretability is about understanding the "how" of a model, focusing on its internal workings. Explainable AI (XAI) is about translating that "how" into a human-understandable explanation of the "why" behind the model's decisions.

Both machine interpretability and AI explainability are crucial for building trust and ensuring responsible AI development. While interpretable models might be inherently more explainable, XAI techniques are constantly evolving to make even complex AI models understandable to humans. As the field progresses, the lines between these concepts might blur further, with the ultimate goal of creating AI systems that are not only powerful but also transparent and accountable.