A. Introduction to AI Models
In the previous unit, we learned that AI models are created by feeding data into algorithms to help machines learn patterns and make predictions.
In this unit, we will go deeper into modeling, understand different types of learning, and explore black box vs. transparent AI models.
B. What is a Model?
A model is the final outcome after training a machine learning algorithm on data.
It is used to make predictions or decisions based on new input data.
Example: A trained AI model can predict whether an email is spam or not.
C. Types of Learning in AI Models
There are two main types of learning used to build AI models:
1. Supervised Learning
- The data has both input and output.
- The model is trained with correct answers (labels).
- The model learns to map inputs to correct outputs.
Example:
If you have data about hours studied (input) and test scores (output), you can train a model to predict test scores.
Common Algorithms:
- Linear Regression
- Decision Tree
- Support Vector Machine (SVM)
2. Unsupervised Learning
- The data has only inputs, no output labels.
- The model finds hidden patterns or groups in the data.
- Used for clustering, pattern detection, etc.
Example:
Grouping customers based on buying habits without knowing their categories.
Common Algorithms:
- K-Means Clustering
- Hierarchical Clustering
- PCA (Principal Component Analysis)
D. Black Box vs. Transparent AI Models
1. Black Box Models
- The internal working of the model is not easily understood by humans.
- Difficult to explain how the model made a decision.
- Often more accurate but less explainable.
Examples: Neural Networks, Deep Learning Models
Problem: If an AI model rejects a loan application, it should explain why — but black box models can’t easily do that.
2. Transparent (Explainable) Models
- The working of the model is clear and easy to understand.
- Decisions can be explained in simple terms.
- Easier to trust and correct.
Examples: Decision Trees, Linear Regression
Importance: In school settings or medical fields, it’s better to use transparent models where reasons for decisions must be known.
E. Concept of Bias in AI Models
AI models learn from data. If the data is biased, the model also becomes biased.
Types of Bias:
- Data Bias: Data collected may not represent all types of users.
- Algorithmic Bias: The model may give more importance to certain features unfairly.
- Human Bias: Mistakes made while collecting or labeling data.
Example: A facial recognition system trained mostly on photos of light-skinned people may not work well for dark-skinned individuals.
F. Training, Testing, and Validation
- Training Data: Used to teach the model.
- Testing Data: Used to evaluate the model’s performance.
- Validation Data: Used to fine-tune the model during training (optional).
Why split data?
To avoid overfitting – a model that only memorizes the training data and performs poorly on new data.
G. Overfitting and Underfitting
Overfitting
- The model works well on training data but poorly on new data.
- It has memorized the data, not learned from it.
Underfitting
- The model is too simple and cannot understand the data.
- Gives poor results even on training data.
Goal: Create a balanced model that performs well on both training and testing data.
H. Real-Life Example: Spam Email Classifier
| Type | Description |
|---|---|
| Input data | Email content (words, phrases) |
| Label (output) | Spam or Not Spam |
| Learning type | Supervised Learning |
| Model type | Transparent (if using Decision Tree), Black Box (if using Deep Learning) |
| Bias to avoid | Avoid training only on certain types of emails |
I. Activity Suggestion (for Class)
Ask students to collect a small dataset:
- Feature: Study time (in hours)
- Label: Marks in test
Using this data, create a simple supervised model and plot a line graph to show how marks increase with study time.
Then explain:
- What happens if data has errors?
- Can we explain how the model is making predictions?
J. Keywords to Remember
| Term | Meaning |
|---|---|
| Supervised Learning | Learning from data with input and correct output (label) |
| Unsupervised Learning | Learning from data with no labels; model finds patterns on its own |
| Black Box Model | A model whose working is difficult to understand |
| Transparent Model | A model whose working is easy to understand |
| Bias | Unfair preference or treatment built into the model |
| Overfitting | Model performs well on training data but poorly on new data |
| Underfitting | Model fails to capture patterns even in training data |
K. Summary of the Unit
- AI models learn from data through supervised or unsupervised learning.
- Transparent models help build trust and are easier to explain.
- Black box models are more complex and harder to interpret.
- Bias in data can lead to unfair AI decisions.
- We must aim to build fair, balanced, and explainable AI systems.