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Cloud Security

What Is Explainability?

5 min. read
Table of Contents

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

Understanding AI models involves interpreting their decision-making processes and outcomes. This can be achieved through techniques like feature importance, partial dependence plots, or using explainable AI (XAI) methods. Visualization tools can also aid in understanding complex models by representing data features, model architecture, or performance metrics. Finally, understanding AI models deeply involves comprehending the problem domain, the data used, and the specific algorithms employed.
Model interpretability in the realm of AI refers to the extent to which a machine learning model's behavior and predictions can be comprehended by humans. An interpretable model allows us to understand the underlying relationships it captures from the data and the logic behind its decisions. This is critical for building trust, facilitating debugging, ensuring fair decision-making, and meeting regulatory requirements in sectors like finance and healthcare.
Transparency in AI involves making the operations and decision-making processes of AI systems clear and understandable to humans. It's not just about opening the 'black box' of complex algorithms, but also about providing clear documentation, disclosing the limitations of the AI, and being open about data usage and privacy. AI transparency is key to fostering trust among users and stakeholders, and it's often required for ethical and legal compliance.
AI decision-making works through a process of learning from data, recognizing patterns, and making predictions or decisions based on these patterns. It begins with training a model on a dataset, during which the model learns the relationship between input features and the target outcome. Once trained, the model can make decisions or predictions on new, unseen data. The specific mechanisms of decision-making depend on the type of AI model, ranging from simple rule-based systems to complex deep learning networks.
Trust in AI refers to the confidence users and stakeholders have in the reliability, safety, and fairness of an AI system. It involves believing that the AI will function as intended, won't cause harm, will make fair and unbiased decisions, and will handle data responsibly. Trust is influenced by factors like the AI's transparency, its performance over time, how well it's been tested, and the reputation of the organization deploying the AI.
Accountability in AI refers to the responsibility and liability of the parties involved in developing and deploying AI systems. It means that if an AI system causes harm or behaves inappropriately, the developers, operators, or owners can be held responsible. Accountability mechanisms can include regulatory compliance, ethical guidelines, auditing, and transparency measures. It's a key aspect of ensuring ethical AI practices and maintaining public trust in AI systems.
Feature importance refers to the contribution each input variable or feature makes to the predictive performance of a machine learning model. Determining feature importance can help to understand the model better, reduce dimensionality, and improve model interpretability. Techniques for assessing feature importance vary depending on the model type and can include permutation importance, Gini importance, or coefficients in linear models.
Partial dependence plots (PDPs) visualize the relationship between a subset of input features and the predicted outcome in a machine learning model, holding all other features constant. PDPs help to interpret complex models by showing whether the relationship between the target and a feature is linear, monotonic, or more complex. By averaging the model's predictions over the distribution of the other features, they offer insights into the effect of a given feature across the range of its values, independent of the distribution of features in the dataset.
A black-box model in AI is a system where the internal workings are not fully visible or understandable to the user. The term refers to the opaqueness of complex models, such as deep learning networks, where the relationship between input and output is not easily interpretable. While these models can be highly accurate, their lack of transparency can pose challenges for trust, accountability, and debugging.
A white-box model, in contrast to a black-box model, is an AI system where the internal workings are fully visible and understandable. These models, such as decision trees or linear regression, allow users to see the exact decision path or mathematical relationships used to arrive at a prediction. While they may not always deliver the highest predictive accuracy, their transparency is valuable for interpretability, trust, and regulatory compliance.
Deep learning is a subset of machine learning inspired by the structure and function of the human brain. It uses artificial neural networks with multiple layers to model complex patterns in data. Deep learning models are capable of learning directly from raw data and can automatically extract useful features. They excel in tasks like image recognition, natural language processing, and any scenario where large, complex datasets are involved.
Neural networks, inspired by biological neural networks, consist of interconnected nodes or 'neurons' organized into layers — input, hidden, and output. During training, data is fed into the input layer, and each neuron in the hidden layers applies a set of weights and a non-linear activation function to the inputs. The process is repeated layer by layer until the output layer is reached. The network learns by adjusting weights to minimize the difference between its prediction and the actual result, using a process called backpropagation.
Predictive analytics involves using data, statistical algorithms, and machine learning techniques to predict future outcomes or trends based on historical data. It allows organizations to forecast events, behaviors, and results with a degree of certainty. Predictive analytics is used across industries for tasks like customer churn prediction, demand forecasting, fraud detection, and risk management. It's a key tool for data-driven decision making.
Detecting bias in AI involves examining both the data used to train the model and the predictions made by the model. Techniques include statistical tests to identify skewed data, examining model performance across different demographic groups, and using tools like AI Fairness 360 or Fairlearn. Bias detection is a proactive step toward ensuring fairness and avoiding discriminatory outcomes in AI systems.
Ethical AI refers to the practice of designing, developing, and deploying AI systems in a manner that respects human rights, fairness, and transparency, and minimizes harm. It involves considerations like mitigating bias, ensuring privacy and security, maintaining accountability, and being transparent about AI capabilities and limitations. Ethical AI aims to ensure AI technologies benefit humanity while minimizing negative impacts.
Model validation is the process of evaluating an AI model's performance using a separate validation dataset unseen during training. It tests the model's ability to generalize to new data. Techniques include cross-validation, holdout validation, and bootstrapping. Performance metrics like accuracy, precision, recall, and F1 score are used, appropriate to the task at hand. It ensures the model is robust and reliable before deployment.
Algorithmic fairness refers to the concept that an AI system should make decisions without unjustified differential outcomes for different groups. It seeks to prevent discrimination based on sensitive characteristics like race, gender, or age. Techniques to achieve fairness include pre-processing the data to remove biases, adjusting the model during training, or post-processing the model's predictions.
Regulatory compliance in AI involves adhering to laws and regulations relevant to AI development and deployment. It can affect various aspects of AI, such as how data is collected and used, transparency requirements, and measures to prevent discrimination. Non-compliance can result in legal penalties, reputational damage, and loss of user trust. Regulations like GDPR in Europe have specific provisions related to AI and data privacy.
LIME (Local Interpretable Model-Agnostic Explanations) is a technique for explaining the predictions of any machine learning model. LIME generates explanations by perturbing the input data and observing the effect on the model's output. It provides a local interpretation for individual predictions, making it easier to understand why a model made a specific decision. It's an important tool for model interpretability and transparency.
Determining if a model is explainable involves evaluating its transparency and the comprehensibility of its decision-making process. Some key factors include the model's complexity, the availability of interpretability techniques, and the ability to provide insights into the relationships between input features and predictions. If the model's inner mechanisms can readily be understood, and if it allows for meaningful explanations of its decisions, it’s considered explainable.

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.

SHAP (SHapley Additive exPlanations) is a unified measure of feature importance for machine learning models, rooted in cooperative game theory. SHAP assigns each feature an importance value for a particular prediction, indicating how much each feature in the dataset contributed to the prediction. It's model-agnostic and provides consistent and locally accurate attributions. By using SHAP values, we can interpret the decision-making process of complex models, enhancing transparency and trust.
Counterfactual explanations in AI provide insights into model decisions by describing what factors would need to change for a model's decision to be different. In simpler terms, it answers the question: "What changes in input variables would lead to a different prediction?" Counterfactual explanations are particularly useful in understanding individual predictions of complex models. They can help expose biases, debug models, and provide users with actionable feedback. They are an important tool in the realm of explainable AI.

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