1. The technicalities of XAI
Great work on the course content so far. We will now go over the technicalities of XAI. This will expand our understanding beyond the role of XAI as translator between AI systems and human understanding.
2. Explainability
"Explainability" in XAI is a flexible concept. What is clear to one might be vague to another. Let's say we are using an AI system for credit scoring, and we are denied a loan. A statistician might be able to interpret the percentages of how each feature contributed. For instance, the credit history contributed 18% in denying a loan, and the debt-to-income ratio contributed 40%.
3. Explainability
For someone else, this might be less straightforward, and a more visual representation is more understandable.
4. Embracing XAI's limitations
While the goal of XAI is admirable, it's important to recognize its limitations. Certain advanced AI models, particularly in deep learning, are inherently complex and resist interpretation. A complex model in AI is also commonly referred to as a black box model.
A black box model in AI is a type of model where the internal workings or decision-making process are not visible or easily understood by humans. It's like a complex machine where you can see what goes in and what comes out, but not how the machine processes the input to produce the output.
For each model that we use, there is a trade-off between the performance and the explainability.
5. Balance between complexity and interpretability
In exploring the balance between model performance and transparency, it's crucial to recognize that not all powerful models sacrifice interpretability.
While it's true that advanced, intricate models often achieve higher accuracy, this doesn't invariably make explainability a casualty. In fact, certain modeling techniques may offer slightly lesser accuracy but significantly greater interpretability. This nuanced trade-off highlights the importance of decision-making in XAI: choosing the right model involves weighing not just the need for precision but also the value of understandability.
Understanding when and how to make this trade-off is a pivotal aspect of mastering XAI, ensuring we harness AI's power without losing sight of its workings.
6. Techniques in XAI
XAI strives for explainability through two primary avenues. On one hand, it utilizes inherently interpretable models such as decision trees and linear regression. These models are transparent by design, offering clear insights into their decision-making logic, in stark contrast to the opaque nature of 'black box' models. With interpretable models, each step of the reasoning process is traceable and understandable.
On the other hand, for models that are not intrinsically interpretable, XAI employs specific techniques to shed light on their decision-making processes. Techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are pivotal in this context. They provide estimations of a model's reasoning without altering the model itself, enabling us to interpret the decision-making processes of otherwise opaque models. These methodologies will be explored in greater detail in the following chapter, highlighting their role in making complex models more transparent and understandable.
7. Let's practice!
Now that we've acquainted ourselves with the key concepts and terminology of XAI, it's time to put this knowledge into action with some engaging exercises. Remember, XAI is not just about understanding AI, it's about making AI understandable to us.