Toward Explainable AI (Part 2): Bridging Theory and Practice—The Two Major Categories of Explainable AI Techniques
In the realm of Artificial Intelligence (AI), the quest for transparency and interpretability is gaining momentum. As we delve deeper into the world of Explainable AI (XAI), it becomes essential to understand the two major categories of techniques that are instrumental in bridging the gap between theory and practice.
1. Model-Specific Techniques:
Model-specific techniques focus on providing explanations based on the internal workings of a particular AI model. These techniques aim to unravel the black box of complex algorithms and make their decision-making processes more transparent.
For instance, techniques such as feature attribution methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) help in understanding how individual features contribute to the model’s predictions. By highlighting the significance of each feature, these techniques offer insights into the model’s decision-making process.
2. Model-Agnostic Techniques:
On the other hand, model-agnostic techniques are designed to provide explanations that are independent of the underlying AI model. These techniques offer a more generalized approach to explainability, making them adaptable to a wide range of AI models.
One prominent example of a model-agnostic technique is the use of surrogate models. Surrogate models are simpler, interpretable models that approximate the behavior of complex AI models. By analyzing the surrogate model’s decisions, users can gain valuable insights into the underlying AI model’s reasoning.
By combining both model-specific and model-agnostic techniques, organizations can achieve a comprehensive understanding of their AI systems’ behavior. This hybrid approach not only enhances transparency but also enables stakeholders to trust AI-driven decisions and take appropriate actions based on the explanations provided.
In conclusion, the two major categories of Explainable AI techniques—model-specific and model-agnostic—play a crucial role in making AI systems more interpretable and accountable. By leveraging these techniques, organizations can bridge the gap between AI theory and practice, paving the way for the widespread adoption of transparent and trustworthy AI solutions.
Stay tuned for the next part of our series as we explore real-world applications of Explainable AI and showcase how these techniques are revolutionizing various industries.
Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases.
Previously, in Part I: Why AI Needs to Be Explainable: Understanding the risks of opaque AI.