Explainable AI vs. Expert Systems vs. LLMs

- Overview

We are currently experiencing an AI hype (again). Enterprises have high hopes for artificial intelligence (AI) and must decide the scope and form of future use of AI. 

In addition to the joy, some people have also raised questions: How does AI "think" and make decisions, what results does it produce, and what data can it access during the process? For many decision-makers, the way AI works lacks explainability. 

This can have fatal consequences in practice, as different techniques produce different results. Decision makers require a certain level of understanding to choose the right AI technology for their problem.

Explainable AI, Expert Systems, and Large Language Models (LLMs) represent different eras and methods of machine intelligence. Expert Systems use hardcoded "if-then" rules from human specialists, LLMs use statistical predictions across vast data, and Explainable AI provides tools to decode complex or black-box model decisions. 

1. Core Definitions:

• Explainable AI (XAI): A set of tools and methods like feature importance or saliency maps that help humans understand how machine learning models reach a specific output. 
• Expert Systems: Traditional symbolic AI programs built with a fixed knowledge base and an inference engine to mimic human decisions in narrow fields using explicit logic. 
• Large Language Models (LLMs): Deep learning neural networks trained on massive text corpora that recognize statistical patterns to generate human-like language and reason broadly. 

2. Key Operational Differences:

• Knowledge Source: Expert systems rely on manual rules written by human experts. LLMs learn implicit patterns from massive unlabelled datasets. XAI analyzes already-trained models to reveal driving factors. 
• Transparency: Expert systems are inherently transparent because their rule paths can be traced. LLMs are classic "black boxes" whose internal weights defy direct human reading. XAI bridges this gap by estimating why a black-box model made a choice.
• Flexibility and Scope: Expert systems are rigid and fail outside their tiny programmed domain. LLMs adapt across diverse, generalized topics, though they can hallucinate facts. 

Please refer to the following for more inforation:

• Wikipedia: Explainable Artificial Intelligence
• Wikipedia: Large Language Model
• Wikipedia Expert Systm

- Expert Systems vs. LLMs

Expert systems use fixed rules and human-defined facts for precise, explainable logic in narrow domains. Large Language Models (LLMs) use statistical patterns from massive text data to handle broad, flexible tasks, though they lack strict determinism and explicit reasoning trails. 

Key Differences: 

1. Knowledge and Reasoning:

• Expert systems: Use hardcoded IF-THEN rules and a strict knowledge base.
• LLMs: Predict next words using statistical weights learned from vast text data. 

2. Flexibility and Scope:

• Expert systems: Limited to narrow, specific fields like medical diagnoses or tax codes.
• LLMs: Adapt to open-ended, creative, and wide-ranging topics.

3. Explainability and Reliability:

• Expert systems: Provide clear logic paths and consistent, deterministic outcomes.
• LLMs: Offer opaque decision-making and can produce variable results for identical prompts. 

Expert systems originated in the 1970s as the first attempt to use technology to mimic human decision-making. They consist of two main components: a knowledge base that contains facts and rules, and an inference engine that draws conclusions or makes decisions. 

LLMs, like ChatGPT, can help agents solve complex decision-making tasks by encoding a large amount of world knowledge from text datasets. They're also good at summarizing long text, such as articles, research papers, or news reports, by extracting key information and providing concise summaries. 

The deterministic nature of AI expert systems also ensures consistent results for the same inputs, which simplifies the explanation of decisions and outcomes. LLMs, on the other hand, operate with huge, constantly expanding amounts of data.

- Explainable AI

Explainable Artificial Intelligence (XAI) is a framework of techniques and processes designed to make the output of machine learning (ML) algorithms transparent and understandable to human users. It directly addresses the "black box" problem of AI by clarifying the specific logic, data features, and reasoning that drive automated outcomes. 

1. Key Benefits of XAI:

• Fosters Trust: Opens the algorithmic black box so users can confidently rely on model predictions.
• Simplifies Debugging: Helps developers quickly isolate model errors, performance gaps, and data drift.
• Ensures Compliance: Satisfies legal frameworks like the GDPR Right to Explanation policy.
• Mitigates Bias: Exposes hidden prejudices within training data to promote ethical, fair decision-making. 

2. Core Techniques & Implementation Styles

• Inherent Interpretability: Using naturally transparent models like shallow decision trees or linear regressions.
• Post-Hoc Explanations: Applying analytical methods to complex models after they have finished training.
• Global Explanations: Detailing the overarching logic and behavior governing the entire AI system.
• Local Explanations: Pinpointing the exact reasons why the model made one specific individual prediction. 

3. Prominent XAI Tools & Frameworks:

• SHAP (SHapley Additive exPlanations): Uses game theory to mathematically calculate how much each feature contributed to a final prediction. 
• LIME (Local Interpretable Model-agnostic Explanations): Perturbs data samples to build a local, understandable model around a specific black-box prediction. 
• Google Cloud Vertex AI: Generates built-in feature attributions directly inside corporate cloud ML workflows.
• What-If Tool: Offers an interactive visual interface to test hypothetical data shifts and analyze model behavior. 

Explainable artificial intelligence (XAI) is a set of tools and processes that help users understand the output of machine learning (ML) algorithms. It can help users trust the results and decisions of AI models, and can be used to improve model performance and debug models. XAI can also help users meet regulatory requirements, such as the General Data Protection Regulation (GDPR), which requires AI systems to provide understandable explanations for their decisions. 

XAI can be used to describe an AI model, its expected impact, and potential biases. Explanations can be targeted at users, operators, or developers, and can help address concerns such as user adoption, governance, and systems development.