What Is Explainable AI?
Explainable AI (XAI) refers to methods and techniques that aim to make the decisions of artificial intelligence systems understood by humans. It offers an explanation of the internal decision-making processes of a machine or AI model. This is in contrast to the 'black box' model of AI, where the decision-making process remains opaque and inscrutable.
XAI is about making AI decisions transparent, accountable and trustworthy. It aims to ensure that AI technologies offer explanations that can be easily comprehended by its users, ranging from developers and business stakeholders to end-users. It bridges the gap between AI and human understanding.
The concept of XAI is not new, but it has gained significant attention in recent years due to the increasing complexity of AI models, their growing impact on society, and the necessity for transparency in AI-driven decision-making.
In this article:
Why Is Explainability Important?
As AI continues to permeate various aspects of life, ethical considerations have become more important. AI systems often make decisions that impact people's lives directly, from healthcare recommendations to financial loan approvals. The ability to understand and explain these decisions is a major ethical concern.
When an AI system makes a decision, it should be possible to explain why it made that decision, especially when the decision could have serious implications. For instance, if an AI system denies a loan application, the applicant has a right to know why. It's in these situations where XAI plays a role in ensuring fairness and equity.
As the usage of AI expands, so does the scrutiny from regulators. In many jurisdictions, there are already regulations in place that require organizations to explain their algorithmic decision-making processes.
For example, under the European Union's General Data Protection Regulation (GDPR), individuals have a “right to explanation”—the right to know how decisions that affect them are being made. This includes decisions made by AI. Therefore, companies using AI in these regions need to ensure that their AI systems can provide clear and concise explanations for their decisions.
Trust and Adoption
Many people are skeptical about AI due to the ambiguity surrounding its decision-making processes. If AI remains a 'black box', it will be difficult to build trust with users and stakeholders.
XAI can help build this trust by providing transparency in AI’s decision-making processes. When people understand how AI makes decisions, they are more likely to trust it and adopt AI-driven solutions.
3 Principles of Explainable AI
1. Explainable Data
Explainable data refers to the ability to understand and explain the data used by an AI model. This includes knowing where the data came from, how it was collected, and how it was processed before being fed into the AI model. Without explainable data, it's challenging to understand how the AI model works and how it makes decisions.
2. Explainable Predictions
An explainable AI model should provide detailed and understandable explanations for its predictions. This includes explaining why the model made a specific prediction and what factors influenced that prediction.
For instance, if a healthcare AI model predicts a high risk of diabetes for a patient, it should be able to explain why it made that prediction. This could be due to factors such as the patient's age, weight, and family history of diabetes.
3. Explainable Algorithms
Explainable algorithms are designed to provide clear explanations of their decision-making processes. This includes explaining how the algorithm uses input data to make decisions and how different factors influence these decisions. The decision-making process of the algorithm should be open and transparent, allowing users and stakeholders to understand how decisions are made.
Learn more in our detailed guide to explainable AI principles (coming soon)
Explainable AI vs. Responsible AI
While explainable AI focuses on making the decision-making processes of AI understandable, responsible AI is a broader concept that involves ensuring that AI is used in a manner that is ethical, fair, and transparent. Responsible AI encompasses several aspects, including fairness, transparency, privacy, and accountability.
XAI is one part of responsible AI. In the context of responsible AI, XAI is used to ensure that AI systems are designed and used in a way that:
- Respects human rights and values
- Does not discriminate against certain groups or individuals
- Respects user privacy
- Decisions are transparent and accountable
Use Cases and Examples of Explainable AI
In the healthcare sector, explainable AI is important when diagnosing diseases, predicting patient outcomes, and recommending treatments. For instance, an XAI model can analyze a patient's medical history, genetic information, and lifestyle factors to predict the risk of certain diseases. The model can also explain why it made a specific prediction, detailing the data it used and the factors that led to a specific decision, helping doctors make informed decisions.
Learn more in our detailed guide to explainable AI in healthcare (coming soon)
In manufacturing, explainable AI can be used to improve product quality, optimize production processes, and reduce costs. For example, an XAI model can analyze production data to identify factors that affect product quality. The model can explain why certain factors influence product quality, helping manufacturers analyze their process and understand if the model’s suggestions are worth implementing.
Explainable AI is crucial for ensuring safety of autonomous vehicles and building user trust. An XAI model can analyze sensor data to make driving decisions, such as when to brake, accelerate, or change lanes. The model can also explain why it made a specific driving decision. This is critical when autonomous vehicles are involved in accidents, where there is a moral and legal need to understand who or what caused the damage.
Explainable AI can help identify fraudulent transactions and explain why a transaction is considered fraudulent. This can help financial institutions detect fraud more accurately and take appropriate action. The ability to explain why a transaction is considered fraudulent can also help in regulatory compliance and dispute resolution.
Learn more in our detailed guide to explainable AI examples (coming soon)
Explainable AI Models and Methods
Local Interpretable Model-Agnostic Explanation (LIME)
LIME is an approach that explains the predictions of any classifier in an understandable and interpretable manner. It does so by approximating the model locally around the prediction point.
LIME generates a new dataset consisting of perturbed instances, obtains the corresponding predictions, and then trains a simple model on this new dataset. This model is interpretable and provides insights into how the original complex model behaves for specific instances. LIME is particularly useful when you need to understand the reasoning behind individual predictions.
Shapley Additive Explanations (SHAP)
SHAP provides a unified measure of feature importance for individual predictions. It assigns each feature an importance value for a particular prediction, based on the concept of Shapley values from cooperative game theory. It's a fair way of attributing the contribution of each feature to the prediction.
SHAP values have a solid theoretical foundation, are consistent, and provide high interpretability. You can use them to visualize the impact of different features on the model prediction, which aids in understanding the model's behavior.
Morris Sensitivity Analysis
Morris Sensitivity Analysis is a global sensitivity analysis technique that identifies influential parameters in a model. It works by systematically varying one parameter at a time and observing the effect on the model output. It's a computationally efficient method that provides qualitative information about the importance of parameters.
This method can serve as a first step when you're trying to understand a complex AI model. It helps you identify the key parameters that significantly impact the model output, thus reducing the complexity of the model and making it more interpretable.
Learn more in our detailed guides to:
- Explainable AI methods (coming soon)
- Explainable AI models (coming soon)
Contrastive Explanation Method (CEM)
CEM is a post-hoc local interpretability method that provides contrastive explanations for individual predictions. It does this by identifying a minimal set of features that, if changed, would alter the model's prediction. These are known as pertinent positives and pertinent negatives.
CEM can be useful when you need to understand why a model made a specific prediction and what could have led to a different outcome. For instance, in a loan approval scenario, it can explain why an application was rejected and what changes could lead to approval, providing actionable insights.
Scalable Bayesian Rule Lists (SBRL)
SBRL is a Bayesian machine learning method that produces interpretable rule lists. It's like a decision tree, but in the form of a list of IF-THEN rules. These rule lists are easy to understand and provide clear explanations for predictions.
SBRL can be suitable when you need a model with high interpretability without compromising on accuracy.
Key Benefits of Explainable AI
Here are the practical benefits organizations should aim to achieve when implementing explainable AI practices and technologies.
1. Improve Fairness and Reduce Bias
Explainable AI allows for early identification of biases embedded in the model. For instance, if a hiring algorithm consistently disfavors candidates from a particular demographic, explainable AI can show which variables are disproportionately affecting the outcomes. Once these biases are exposed, they can be corrected, either by retraining the model or by implementing additional fairness constraints.
Furthermore, by providing the means to scrutinize the model's decisions, explainable AI allows external audits. Regulatory bodies or third-party experts can assess the model's fairness, ensuring compliance with ethical standards and anti-discrimination laws. This creates an additional layer of accountability, making it easier for organizations to foster fair AI practices.
2. Mitigate Model Drift
Model drift is a challenge that often emerges in real-world AI applications. As the data landscape changes, the model’s understanding could become outdated, leading to decreased performance. Explainable AI offers insights into how the model is interpreting new data and making decisions based on it. For example, if a financial fraud detection model starts to produce more false positives, the insights gained from explainable AI can pinpoint which features are causing the shift in behavior.
Armed with this understanding, data scientists and engineers can take proactive steps to recalibrate or even redesign the AI model to adapt to the new data landscape. They can also implement monitoring mechanisms that alert them when the model's explanations deviate significantly, indicating a likely occurrence of model drift. This ensures that the model remains reliable and accurate over time.
3. Manage and Minimize Model Risk
Understanding the limitations and the scope of an AI model is crucial for risk management. Explainable AI offers a detailed overview of how a model arrives at its conclusions, thereby shedding light on its limitations. For instance, if a predictive maintenance model for industrial machinery frequently fails to account for certain types of mechanical failures, the explanations can show which variables or features the model is not considering adequately.
Additionally, explainable AI contributes to a granular understanding of model uncertainty. By dissecting how different features and data points contribute to a decision, stakeholders can judge the confidence level of each prediction. If a critical business decision is based on a model's output, understanding the model's level of certainty can be invaluable. This empowers organizations to manage risks more effectively by combining AI insights with human judgment.
Explainable AI with Kolena
Kolena is a machine learning testing and validation platform that solves one of AI’s biggest problems: the lack of trust in model effectiveness. The use cases for AI are enormous, but AI lacks trust from both builders and the public. It is our responsibility to build that trust with full transparency and explainability of ML model performance, not just from a high-level aggregate ‘accuracy’ number, but from rigorous testing and evaluation at scenario levels.
With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes, not weeks. Kolena’s AI/ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.