Explainable AI: How AI is becoming more interpretable?

Many models have created new content, predicted future sales, and classified and recognized images. But have you ever wondered how these models make these decisions? 🤔 

Well, this question is affecting a lot of people, especially at a time when AI is so heavily involved in decision-making. 

This is where Explainable AI(XAI) comes into the picture XAI is the emerging field that tries to make AI more transparent, interpretable, and trustworthy. 

Having a powerful model is not enough; we must understand it. 

Whether it’s a medical diagnosis or a loan approval, the reasoning behind AI decisions matters, and XAI is the key to unlocking these insights.

How does XAI work? 

There are multiple popular techniques to understand how an AI model makes decisions — some of them are: 

1. Feature importance: In this technique, we can get an insight into what features were used to make the decision. For example, if a patient is diagnosed with melanoma a type of skin cancer, did the model take into consideration of bio-markers or did it used age or gender which are not the most apt features for classification? 

2.Shapley Additive explanations(SHAP): This technique is based on game theory, Game theory is the study of strategic decision-making, often used to analyze how different players contribute to a collective outcome. In the context of XAI, each feature (or input variable) in a model is treated as a “player” contributing to the overall “game” of making predictions. 

3.Saliency Maps: For image classification purposes saliency maps are used as they can point out the part of the image which influenced the classification. For example, if a model classifies an image as cat then by saliency maps we can find out what part of the image led model to predict it as a cat. 

4.Counterfactual Explanations: It works on what if questions and answers. For example, if a banking AI rejects your loan request, it could tell you that if your credit score was 70 points higher, the loan would have been approved. 

5.Local Interpretable Model-agnostic Explanations(LIME): LIME is like asking a chef: “Why did you add cinnamon to this dessert?” Chef: “Because it enhances sweetness and complements the apples.” LIME provides similar explanations for machine learning models. 

Image on XAI by GoML

The Human-AI collaboration

Due to XAI, decision-makers can now be well aware of why their AI model is giving a certain prediction and whether they can trust it or not. 

AI is no longer a black box that takes numbers and churns out some results, with the help of XAI the AI-human collaboration has increased. 

 XAI encourages users to ask the right questions, fine-tune models, and ensure fairness. 

It also helps developers identify if the AI system is unintentionally biased or flawed. 

Challenges in XAI 

While XAI promises transparency, there are challenges: 

• Complexity vs. Simplicity: More accurate models (like deep learning) are often less interpretable. Simplifying them might reduce their power, so there’s a delicate balance. 
• Time & Resource-Intensive: Adding explainability layers can require extra computation and engineering effort. 

Future Work

In the future, we can expect: 

1. Regulatory Frameworks: Global regulations requiring AI systems to explain their decisions, especially in sensitive areas like finance and healthcare. 

2. User-Centric AI: AI designed with explainability in mind from the start, not as an afterthought. 

3.Greater Trust in AI: When people can trust and understand AI decisions, its adoption will accelerate across industries. 

Conclusion 

Explainable AI is a necessity that every AI user and developer must be aware of, especially in industries such as finance and healthcare. Any bias or discrimination should not be perpetuated by the models and we must know why a decision was made; banks and other institutions can’t simply say, “Our AI model said you should not get a loan so you are not getting one”, to make industries accountable we need XAI.