Introduction
Explainable AI (XAI) seeks to shed light on the decision-making processes of AI models. Several techniques have been developed to achieve interpretability, ranging from inherently interpretable models to post-hoc explanations for complex models.
- Linear RegressionOutputs can be easily explained by the weights (coefficients) assigned to each input feature.
- Decision TreesDecision-making is structured as a tree, where nodes represent decisions based on feature values, making the process transparent.
- Rule-Based SystemsDecisions are derived from a predefined set of rules, offering clear logic for each outcome.
- Global Importance: Indicates the overall importance of each feature in the model. Techniques include permutation importance, where feature values are shuffled to observe the impact on model performance.
- Local Importance: Explains the importance of features for a specific prediction. This is typically used with complex models to provide instance-specific explanations.
- Description: Train a simpler, interpretable model (the surrogate) to approximate the decisions of a complex model.
- Examples: Training a decision tree (surrogate) to mimic a neural network’s decisions, providing insights into the latter’s decision-making process.
- Description: Explains individual predictions by perturbing the input data, observing the changes in predictions, and fitting a simple model to describe those changes locally.
- Use Case: Can be used with any model, providing local explanations that might vary for different instances.
- Description: Based on cooperative game theory, SHAP values provide a unified measure of feature importance by averaging contributions across all possible feature combinations.
- Advantage: Ensures consistent and fairly distributed feature importance values.
- Description: Visualizes what activates neurons in deep learning models, particularly in image recognition tasks.
- Use Case: Helps in understanding which parts of an input image contribute to a neural network’s decision.
- Description: Explains model decisions by suggesting the smallest change to the input that would alter the model’s prediction.
- Use Case: Useful in scenarios like loan denials, where an applicant might want to know the minimum change in their profile needed to obtain approval.
- Description: For neural networks, especially in image processing, saliency maps highlight regions in the input that had the most influence on the model’s decision.
- Use Case: Identifying which parts of an image were crucial for a classifier’s decision, aiding in model debugging and interpretability.
Conclusion
The quest for interpretability in AI has led to the development of diverse XAI techniques. The choice of technique often depends on the model type, the domain of application, and the target audience for the explanations. As AI systems become more ubiquitous, the fusion of accuracy with interpretability becomes paramount to ensure fairness, trustworthiness, and societal acceptance.
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Fiber Internet
Internet delivered through strands of glass using light. Fiber commonly supports high capacity, low latency, and strong upload performance, but availability must be confirmed for the exact address.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.
Broadband
A general term for always-on, high-speed Internet access. Broadband can be delivered over fiber, cable, DSL, fixed wireless, cellular, or satellite networks.
Bandwidth
The amount of data a connection can carry in a given time, usually measured in Mbps or Gbps. More bandwidth supports more users, devices, and simultaneous applications.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Dedicated Internet Access (DIA)
A business-grade Internet connection with capacity dedicated to the customer rather than shared in the same way as typical consumer broadband. It often includes symmetrical speeds and an SLA.