A Simplified Guide to Explainable AI’s Diverse Models and Techniques
Last Updated on: June 18, 2025
As AI systems grow more complex, understanding how they make decisions is more critical than ever. Explainable AI (XAI) helps uncover the ‘why’ behind AI predictions, making them transparent, accountable, and trustworthy.
Like a detective solving a case, XAI uses a range of techniques to explain AI’s inner workings. Two key distinctions in this toolkit are: model-centric vs. data-centric approaches and model-specific vs. model-agnostic methods.
These techniques help developers, stakeholders, and users gain clarity and confidence in AI outcomes.
In this blog, you’ll learn:
Key Takeaways
Let’s delve deeper with a simple scenario: Imagine you’re exploring a fascinating city, trying to understand everything that makes it beautiful.
Using this scenario, we’ll examine how model-centric and data-centric approaches highlight both AI’s structure and the data shaping its behaviour.
I. XAI Toolkit
1. Model-Centric vs. Data-Centric
Here are the details –
| Model-centric explainability | Data-centric explainability |
| It focuses on understanding the city’s architecture, its streets, buildings, and landmarks. It delves into the model’s structure, logic, and decision-making processes. | It explores the city’s inhabitants and their stories. It examines the data that powers the model. |
| Key features: These are the influential elements shaping predictions, like a city’s most prominent landmarks. | Data patterns and relationships: Uncovering hidden connections and trends, like identifying popular neighbourhoods and cultural hubs. |
| Input interactions: How the model responds to different inputs, like a city’s traffic patterns adapting to rush hour. | Data’s influence on predictions: Understanding how data shapes model outcomes, like how a city’s history influences its present character. |
| Sensitivity to data changes: How the model’s behaviour adapts to variations in data, like a city’s resilience to weather events. | Potential biases and inconsistencies: Detecting unfair or misleading patterns in the data, like addressing social inequalities within the city. |
Together, these approaches create a comprehensive understanding of AI’s decision-making processes, ensuring trust and accountability.
2. Model-Specific vs. Model-Agnostic
Now, let’s consider how XAI techniques interact with different model types.
1. Model-specific XAI
- Built for one model, like deciphering a specific AI’s logic.
- These techniques are custom-designed for particular AI model architectures, like a city guide specialising in historical tours. They offer in-depth insights into specific models, but may not apply to others.
2. Model-agnostic XAI
- These techniques are adaptable explorers, able to navigate diverse model types, like a versatile city guide who can cater to any interest.
- They provide general explanations that can be applied to a wider range of AI models.
By strategically employing these techniques, XAI practitioners can illuminate AI’s reasoning, fostering trust and ensuring responsible AI development. It’s like having a trusted guide who can navigate the complexities of AI, making its decisions transparent and understandable to all.
LIME and SHAP
LIME (Local Interpretable Model-agnostic Explanations)
- Model-Agnostic: It treats the AI model as a ‘black box’ and doesn’t need access to the model’s internal workings.
- How it works: It perturbs the input data and observes how the predictions change to create a simpler, interpretable model (like a linear model) around that prediction.
SHAP (SHapley Additive exPlanations)
- Model-Agnostic by design: SHAP can be applied to any model type but also has model-specific optimisations (like TreeSHAP for tree-based models).
- How it works: It uses concepts from cooperative game theory (Shapley values) to attribute each feature’s contribution to a prediction.
In summary:
- LIME is purely model-agnostic.
- SHAP is primarily model-agnostic, but it also includes model-specific enhancements to improve efficiency for certain models.
Note:
The art of XAI lies in selecting the right techniques for the task at hand and harmonising them to create a comprehensive understanding of AI’s decision-making.
By combining model-specific and model-agnostic approaches, model-centric and data-centric perspectives, and leveraging the strengths of techniques like Lime Explainable AI and SHAP, we can transform AI from a mysterious oracle into a transparent and trusted partner.
II. Conclusion
As AI becomes deeply embedded in critical decision-making, from healthcare and finance to smart cities and education, transparency is no longer optional; it’s essential. By understanding the mechanics of model-centric versus data-centric strategies and distinguishing between model-specific and model-agnostic tools, stakeholders can develop systems that are not only powerful but also ethical, fair, and trustworthy.
Techniques like LIME and SHAP play a pivotal role in this evolution, transforming opaque AI predictions into actionable insights. Ultimately, embracing the full spectrum of XAI methodologies ensures that AI remains a collaborative partner rather than a black-box authority.
Curious to see how Explainable AI works in real-world industries like healthcare, finance, and even climate prediction?
Discover practical use cases and the future of XAI in our next article.
