What Is Explainability?
Explainability in artificial intelligence refers to the ability to describe an AI model's internal workings or outcomes in understandable terms. It makes complex AI decisions transparent and trustworthy. In fields like healthcare or finance, where understanding why a model made a particular decision has implications, explainability has influence. In terms of MLOps and AI security, explainability supports accountability and helps diagnose and rectify model errors.
Businesses increasingly rely on artificial intelligence (AI) systems to make decisions that can significantly affect individual rights, human safety, and critical business operations. But how do these models derive their conclusions? What data do they use? And can we trust the results?
Explainability Defined
AI algorithms are often perceived as black boxes making inexplicable decisions — decisions that in certain applications can impact human safety or rights. Explainability is the concept that a machine learning model and its output can be explained in a way that makes sense to a human at an acceptable level. Certain classes of algorithms, including more traditional machine learning algorithms, tend to be more readily explainable while being potentially less performant. Others, such as deep learning systems, while being more performant, remain much harder to explain.
Encountering an AI model lacking explainability could leave a user less certain of what they knew previous to employing the model. Conversely, explainability increases understanding, trust, and satisfaction as users grasp the AI's decision-making process.
Confusion Response |
Trust Reaction |
Why did it choose this? How did it decide? Can I trust this result? What if it's wrong? Is it considering everything? Does it understand my input? Why not a different answer? Is it guessing? How sure is it? What's it not telling me? |
Ah, now I get it. That makes sense. I see why it chose that. Interesting reasoning. Didn't expect that factor. Clearer than I thought. Good to know the logic. Helps me trust it more. I can follow that. Useful breakdown. |
Techniques such as feature importance analysis, LIME, SHAP, and other interpretability methods contribute to making a model more explainable by offering insights into its decision-making process. Additionally, models that align with regulatory standards for transparency and fairness are more likely to be explainable models.
Why Explainability Matters
Machine learning models, particularly those based on complex algorithms like neural networks, can act as black boxes, obscuring the if/then logic behind their outputs. This opacity can lead to mistrust or skepticism among stakeholders, regulators, and customers who need to understand the basis of decisions impacting them.
In healthcare, for example, an AI system could be employed to assist radiologists by prioritizing cases based on the urgency detected in X-ray images. In addition to performing with high accuracy, the AI system must provide explanations for its rankings to ensure patient safety and comply with medical regulations. In other words, it needs to be transparent enough to reveal the features in the images that led to its conclusions, enabling medical professionals to validate the findings.
Additionally, in jurisdictions with regulations such as the EU's General Data Protection Regulation (GDPR), patients may have the right to understand factors influencing their cases and could challenge decisions made with the aid of AI. In instances such as this, explainability goes beyond technical performance to encompass legal and ethical considerations.
Transparency in AI is requisite to fostering trust, ensuring compliance with regulatory standards, and promoting the responsible use of AI technologies. Without a clear understanding, users may resist adopting AI solutions, stunting potential gains from these innovations.
Explainability Vs. Interpretability
Interpretability and explainability in AI refer to our ability to understand the decisions made by AI models. While these concepts in machine learning are related — both integral to building trust, facilitating debugging and improvement, ensuring fair decision-making, and meeting regulatory requirements — they are distinct.
Interpretability is about the transparency of internal mechanics of AI models. It refers to the degree to which a human can understand and trace the decision-making process of a model. An interpretable model allows us to comprehend how it works internally and how it arrives at its predictions. Interpretability is particularly important for model developers and data scientists who need to ensure their models are working as expected.
Explainability is about the ability to explain the outcomes of an AI model in understandable terms. It's about bridging the gap between the complexity of AI models and the level of understanding of the user, ultimately fostering confidence in the model's outputs. Explainability is especially relevant for the end-users of AI systems who need to understand why a decision was made to trust it. In applications like healthcare or finance, understanding why a model made a particular decision can have serious implications.
Interpretability |
Explainability |
---|---|
The ability to observe the inner mechanics and logic of the model |
Provides explanations for model predictions without necessarily revealing the full internal workings |
Understand exactly why and how the model generates specific predictions |
Uses techniques to analyze and describe model behavior after the fact |
Ability to interpret the model's weights, features, and parameters |
Offers insights into which inputs or features contributed most to a particular prediction |
Interpretable models are inherently explainable, but not all explainable models are fully interpretable.
Explainability, Interpretability, and AI Security
Explainability and interpretability factor into AI security in important ways.
Transparency and Trust
Explainable and interpretable AI systems allow users and stakeholders to understand how decisions are being made, which builds trust and enables better oversight of AI systems. This transparency is crucial for security applications where the consequences of decisions can be significant.
Compliance and Regulation
Regulators and policy-makers are concerned with both interpretability and explainability, as they need to ensure AI systems are compliant with regulations and ethical guidelines and not causing harm or perpetuating biases. When AI systems are explainable and interpretable, it’s easier to identify biases and errors, as well as vulnerabilities that could be exploited for malicious purposes.
Debugging and Improvement
Interpretability allows developers to understand how their models work, making it easier to debug issues and improve system performance and security over time.
User Adoption and Proper Use
In security applications, user trust and proper utilization of AI systems take on a critical level of importance. Explainable AI helps users understand system capabilities and limitations, leading to more appropriate and secure use of security solutions.
Related Article: Steps to Successful AI Adoption in Cybersecurity
Ethical Considerations
As AI systems are increasingly used in high-stakes decision-making, explainability becomes key to ethical use and accountability, both of which are important aspects of overall system security.
Explainability and Adversarial Attacks
While explainability enhances security, it's worth noting that it can potentially make systems vulnerable to adversarial attacks by revealing enough about the inner workings of the AI for adversarial parties to exploit.
Manipulation of Explanations
Attackers can craft inputs that produce misleading or deceptive explanations, even while the model's output remains unchanged. This can undermine trust in the AI system and its explanations.
Reverse Engineering Model Behavior
By analyzing explanations, adversaries may gain insights into the model's decision-making process, allowing them to more effectively craft adversarial examples that fool the model.
Fairwashing
Malicious actors can manipulate explanations to hide unfair or biased behavior of the model. For example, they may alter the model to produce explanations that appear unbiased, even when the underlying decisions are discriminatory.
Targeted Attacks on Explanation Methods
Some attacks specifically target popular explanation techniques like LIME or SHAP, manipulating the model to produce explanations that hide its true reasoning or vulnerabilities.
Exploiting Model Transparency
While explainability aims to increase transparency, it can also reveal vulnerabilities in the model that attackers can exploit to craft more effective adversarial examples.
Social Engineering
Deceptive explanations could be used to manipulate users' trust or decision-making processes in security-sensitive applications.
Data Privacy Risks
Detailed explanations might inadvertently reveal sensitive information about the training data or model architecture.
Mitigating Adversarial Risks
Although explainability and interpretability can introduce security trade-offs, they’re considered essential components of responsible and secure AI development, especially in sensitive applications where understanding the decision-making process helps to provide safety, fairness, and reliability. Just the same, these potential exploitations highlight the need for a balanced approach to explainability in security contexts. MLOps teams must implement carefully to avoid introducing vulnerabilities.
Security Objectives to Prioritize
- Develop robust, manipulation-resistant explanation methods.
- Implementing adversarial training techniques that consider both model outputs and explanations.
- Create evaluation frameworks to assess the security of explainability of AI systems.
- Design explanation methods that balance transparency with security considerations.
As the field of adversarial machine learning evolves, so too must our approaches to secure and trustworthy explainable AI.
Explainable AI: From Theory to Practice
Explainability, as we’ve discussed, refers to the general ability to explain or provide reasons for a model’s output in a way that humans can understand. So what is explainable AI?
Explainable AI (XAI) differs from explainability, in that it’s a subset of AI that focuses on developing AI systems and models that are inherently explainable or interpretable. XAI aims to create AI models and algorithms that can provide clear explanations for their decisions and predictions, making the AI system's behavior more transparent and understandable to humans.
Explainability |
Explainable AI (XAI) |
|
---|---|---|
Implementation |
Explainability can be achieved through various methods, including post-hoc explanations for existing models. |
XAI often involves designing AI systems from the ground up with explainability in mind. |
Objective |
Explainability aims to make any system or process understandable. |
XAI specifically targets the transparency and interpretability of AI models and their decision-making processes. |
Techniques |
Explainability may use general techniques for explaining complex systems. |
XAI employs specialized techniques and algorithms designed for AI systems, such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations). |
XAI is a response to the black box nature of many complex AI models, aiming to increase trust, accountability, and understanding of AI systems.
Explainability FAQs
LIME works by approximating the decision boundary of a complex model with a simple, interpretable one for a specific instance.
- LIME first selects a specific instance for which a prediction explanation is needed.
- It then perturbs the instance, creating a set of 'neighbor' data points around the original instance.
- The complex model's predictions for these new data points are computed.
- LIME fits a simple interpretable model (like a linear model) to these data points and their associated predictions.
- The coefficients of the simple model serve as the explanation of the original model's prediction for the specific instance.
As the simple model is trained locally around the instance of interest, it can provide a good approximation of the complex model's behavior in that local vicinity, providing a local explanation. Even if the overall model is a black box, we can still understand why it makes decisions.