To understand the decisions made by ChatGPT and leverage model explainability in AI, there are several approaches you can use, especially when it comes to explainable AI (XAI) techniques. Here’s how you can apply model explainability in AI to ChatGPT-like models:

1. Token-Level Interpretability:

• Attention Mechanisms: GPT-based models like ChatGPT use attention mechanisms to focus on different parts of the input when generating responses. By visualizing the attention weights, you can identify which words or phrases in your input the model is focusing on to make decisions. Some tools allow you to see token-level attention distributions, showing how the model weighs different parts of the input.

Example: If you ask a question, you could analyze which words in your query the model assigns the most attention, helping you understand which parts of the input influenced the response the most.

2. Post-hoc Explainability:

• SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations): These are post-hoc model explainability techniques that can be applied to interpret specific decisions made by AI models. Although not directly used with large language models like ChatGPT by default, if you have access to a customized or fine-tuned version, you can use SHAP or LIME to explain why certain words or sentences were selected as output based on input features.

Example: You could use SHAP values to analyze which input tokens (words or phrases) had the most influence on the generated response.

3. Behavioral Testing and Error Analysis:

• Input-Output Testing: By running a set of similar inputs and analyzing the outputs, you can derive patterns in how the model makes decisions. For example, you could analyze if small changes in the input (e.g., changing tense, using synonyms) lead to significantly different outputs, which can shed light on the model’s behavior and sensitivity.

Example: You can give similar questions but alter a single word or phrase and observe how the output changes, giving insight into which parts of the input are pivotal in decision-making.

4. Explainability through Training Data Analysis:

• Understanding the Data Sources: For large language models like ChatGPT, explainability can also come from understanding the kinds of data the model was trained on. While ChatGPT itself doesn’t have explicit access to its training data during conversations, knowing the general corpus (e.g., books, websites, conversations) can help you infer why certain biases or knowledge might be present.

Example: If ChatGPT provides a certain answer, you might infer that it’s based on commonly available online data, general knowledge sources, or patterns learned from training on large-scale datasets.

5. Interactive Prompt Engineering for Clarity:

• Prompt Clarification: You can prompt the model to explain its reasoning by asking it to break down its answer into steps. While ChatGPT doesn’t have direct insight into how it generated a specific response at a technical level, asking it to justify its answers can sometimes give you an intuitive explanation of the reasoning process.

Example: If you ask ChatGPT a complex question, you can follow up with, “Why did you arrive at this conclusion?” to get the model to clarify its thought process step by step.

6. Bias and Fairness Audits:

• Bias Detection: By testing the model with a range of diverse inputs, you can assess how its responses vary across different contexts, demographics, or viewpoints. This can help surface any potential biases or areas where the model may be systematically skewed.

Example: You could ask the model questions from different cultural or social perspectives and compare how it responds to detect any potential biases in decision-making.

7. Gradient-Based Explainability:

• Integrated Gradients: Although typically applied in vision models, gradient-based methods like Integrated Gradients can sometimes be adapted to NLP models. This technique calculates the contribution of each input feature (word or phrase) to the final prediction or decision.

8. Custom Explainability Tools:

• OpenAI API with Explainability Features: If you’re using the OpenAI API, you can build custom explainability features by analyzing output token probabilities, confidence scores, and by observing the step-by-step reasoning in code or math-related questions where the model breaks down the process.

By using a combination of these techniques, you can gain insights into how a model like ChatGPT makes decisions and better understand the reasoning or logic behind its responses.

Putting it into Practice

Here’s an example flow using prompts to uncover explainability:

Initial Prompt:

• “Explain why AI is important in modern society.”

Follow-up for Reasoning:

• “Can you explain step by step how you arrived at this answer?”

Highlighting Key Information:

• “What part of my question was most important in determining your response?”

Testing for Assumptions/Bias:

• “Did you make any assumptions in providing this answer?”

Generating Alternatives:

• “Can you provide an alternative explanation?”

By using these prompts, you can simulate an interactive explainability layer directly within your conversation with ChatGPT.