Imagine This...

You’re a bank manager faced with an AI denying loans to qualified applicants. Would you trust it if it couldn’t explain why? Now picture a scenario where your AI assistant generates personalized financial plans for each customer. Which would you prioritize — transparency or creativity?

This question illustrates the distinct purposes of Explainable AI (XAI) and Generative AI (GAI), two revolutionary branches of artificial intelligence that cater to different needs. Let’s delve deeper into what these technologies are, how they work, and their real-world applications.

What is Explainable AI?

Explainable AI refers to models and techniques that make it possible for humans to understand and trust AI decisions. This is especially important in fields where AI predictions directly impact real-world scenarios, such as healthcare, finance, and law.

How it Works: XAI employs techniques like Local Interpretable Model-agnostic Explanations (LIME), SHAP (SHapley Additive exPlanations), and counterfactual explanations. These tools highlight how specific features influence AI predictions, making the process more transparent.

Applications:

• In finance, banks must explain why a loan was approved or denied.

• In healthcare, doctors need to understand why an AI model diagnosed a specific condition or recommended a treatment.

What is Generative AI?

Generative AI creates new data similar to the data it was trained on. This includes generating realistic images, human-like text, or even original music.

How it Works: Generative AI relies on advanced models such as Generative Adversarial Networks (GANs), Transformer-based models like GPT, and Variational Autoencoders (VAEs). These models learn patterns from existing data and use them to produce new, contextually relevant outputs.

Applications:

• Creative industries rely on Generative AI for content generation, from designing art to generating code.

• Tools like ChatGPT, DALL-E, and Photoshop’s generative features enhance productivity and personalization.

Why These Technologies Matter Together

While working on a task management app, I noticed how users demanded both trust in AI predictions and personalized experiences. Explainable AI addressed concerns about why a task was marked ‘high-priority,’ while Generative AI helped craft personalized notifications creatively. This balance between transparency and innovation is evident in many domains, including healthcare and technology. Bringing XAI and GAI together offers a powerful synergy. For example, integrating SHAP into generative tools like Codex could explain why specific outputs were generated, enhancing user trust while fostering creativity.

To better understand the unique roles of Explainable AI and Generative AI, it’s crucial to explore their key differences and how they address distinct user needs.

Key Differences Between XAI and Generative AI

especially when applied to domains like software engineering, as highlighted in the study :

1. Purpose and Output

XAI: Designed to explain how and why AI models make decisions or predictions. The output focuses on insights or transparency, such as highlighting features or inputs that influenced a decision.

Example: Using attention visualizations or uncertainty indicators to explain why an AI suggested specific code for autocompletion.

GenAI: Focuses on creating artifacts like text, images, or code. It generates new, contextually relevant outputs but doesn’t inherently provide explanations for how the output was produced.

Example: A generative code model producing Python code from natural language inputs.

2. User Needs and Interaction

XAI: Tailored to help users understand and trust the AI. The primary focus is on answering user questions, such as:

• “Why did the model make this choice?”

• “What would happen if I changed the input?”

GenAI: Prioritizes usability and creativity, offering tools for tasks like code translation, completion, or natural language to code conversion. Users may seek:

• “What can this model produce?”

• “How can I tweak the input to improve the output?”

To illustrate this difference, an experiment was conducted to compare the usability and outcomes of both approaches:

A. Environment:

For XAI:

• XAI Library: Use SHAP explanation.

• Dataset: creditcard.csv (https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)

For Generative AI: pre-trained model GPT-3

B. Explainable AI (XAI) — Classifier with SHAP:

C. Generative AI (GPT-3)

The integration of Explainable AI (XAI) methods like SHAP brings clarity to the performance of the Generative AI-powered classification model. While the raw metrics from the classification report (e.g., precision, recall, F1-score) indicate how well the model performs, SHAP uncovers the “why” behind the decisions. The SHAP summary plot provides a visual representation of the features contributing to the model’s predictions. Features like V1 and Time exhibit low SHAP interaction values, suggesting limited influence on fraudulent classification

3. Complexity of Explainability

XAI: Generally applied to discriminative models, which are easier to explain as they classify data based on predefined rules or boundaries. XAI techniques like LIME, SHAP, or Anchors are well-suited for such tasks.

GenAI: Explaining generative models is inherently more complex due to the creative, iterative nature of output generation. For instance, explaining how a model like Codex arrived at specific code often involves understanding high-dimensional latent spaces or attention mechanisms, which are harder to interpret for end-users.

Conclusively, from the key differences and the experiment demonstrated above, we can see that users gain a clear understanding of how the model evaluates a transaction’s likelihood of being fraudulent, thereby enhancing trust compared to an opaque Generative AI model that outputs predictions without justification.

The experiment underscores that while Generative AI is powerful for building predictive models, Explainable AI bridges the gap between accuracy and trust. While GenAI shows how well the model performs, XAI explains why it performs the way it does.

As AI continues to shape critical industries, the choice between transparency and creativity need not be binary. By integrating the strengths of XAI and GAI, we can create solutions that inspire trust and innovation alike. What will you prioritize in your next AI project?

References

• J. Sunet al., “Investigating explainability of generative AI for code through scenario-based design,” presented at the University of Southern California, Los Angeles, USA, 2024.

• K. Jayakumar and N. Skandhakumar, “A Visually Interpretable Forensic Deepfake Detection Tool Using Anchors,” 2022 7th International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2022, pp. 1–6, doi: 10.1109/ICITR57877.2022.9993294. keywords: {Training;Deepfakes;Visualization;Digital forensics;Detectors;Pressing;Predictive models;Deepfake Detection;XAI;Computer Vision;Deep Neural Networks;Anchors;Digital Media Forensics},