AI Ethics & Project Cycle: CBSE Class 10 (Bias, Privacy, Scoping & Evaluation)
This comprehensive guide covers the key CBSE Class 10 AI chapters on Ethics and the Project Cycle. Explore moral dilemmas, data privacy, and AI bias, then learn the 5-stage project cycle: Problem Scoping (4Ws), Data Acquisition, Data Exploration, Modelling, and Evaluation with interactive charts.
AI Ethics for Class 10: Understanding the Hard Questions
This page covers the moral issues, data privacy concerns, AI bias, and access challenges relevant to your CBSE curriculum.
1. Moral Dilemmas: The Self-Driving Car Problem
Artificial intelligence is not just a technical tool; it makes decisions. Sometimes, these decisions have no clear “right” answer. These are moral dilemmas. Developers must program AI to respond to these situations.
The most famous example is the “self-driving car” scenario, a version of the trolley problem.
Scenario Poll: What Should the Car Do?
A self-driving car’s brakes fail. It is heading towards a crosswalk with five pedestrians. The car can swerve onto the sidewalk, but it will hit one person standing there.
Option A: Stay Course
The car continues straight, hitting the five pedestrians.
Option B: Swerve
The car swerves, hitting the single person on the sidewalk.
What if the single person on the sidewalk is the car’s owner? What if the pedestrians were crossing against a “Don’t Walk” signal? AI must be programmed with a set of rules for these choices. Your learning outcome is to be able to articulate these trade-offs.
Cross-link: These ethical choices are defined in the Problem Scoping stage of the AI Project Cycle (Section 5), where developers must decide *why* the AI is being built.
2. Data Privacy: Permissions, Sensors, and Consent
AI systems need data to learn. Often, this is your personal data. Data privacy involves your right to control who sees and uses your information. Your smartphone is a powerful data collection tool, using sensors like the microphone, camera, and GPS.
Smartphone App Permissions
Apps often ask for permissions. It is important to know what you are agreeing to.
| Permission Request | Stated Reason | Potential Privacy Concern |
|---|---|---|
| Contacts | “To help you find friends” | The app uploads your entire address book to its servers. |
| Microphone | “To use voice commands” | The app could be listening even when you are not using it. |
| Location | “To provide local weather” | The app can track your movements and build a profile of your daily routine. |
3. AI Bias: When Data Creates Unfairness
AI bias occurs when an AI system produces results that are unfair to certain groups of people. This is not because the AI “thinks” in a biased way. It happens because the AI learns from human-generated data that already contains bias.
- Example (Voice): Early voice assistants were less accurate at understanding female voices. This happened because the training data contained more male voices.
- Example (Search): If you search an online job site for “secretary,” the image results might show mostly women. This reflects old stereotypes present in the data, and the AI learns and repeats this pattern.
Interactive Chart: Visualizing AI Bias
This chart shows a hypothetical scenario for a facial recognition system. The error rate (how often the system is wrong) is not the same for all groups. This is a visual example of bias.
Hypothetical Error Rates in a Facial Recognition Model
4. Access & Inclusion: The AI Digital Divide
Who gets to use and build AI? This question is about access and inclusion. There is a risk of a “digital divide,” where people without access to new technology, fast internet, or proper education are left behind.
Downloadable Debate Kit
Debate is a great way to explore these topics. Use these prompts for a class discussion.
- Topic 1: “AI will create more jobs than it destroys.”
- Topic 2: “Student access to AI writing tools should be restricted.”
Parent & Teacher Guide
This guide helps adults talk to kids and teens about AI. It covers screen time, AI tools in homework, and online safety.
- How to set boundaries for AI tools.
- Identifying AI-generated content.
- Talking about privacy and digital footprint.
5. The AI Project Cycle
Interactive: The 5 Stages of the AI Project Cycle
Creating an AI system is not a single step. It is a process called the AI Project Cycle. This cycle provides a structured way to move from an idea to a working, evaluated solution. The main stages are: Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation.
5.1 Problem Scoping
This is the most important stage. Before writing any code, the team must understand the problem. We use the 4Ws Problem Canvas to guide this.
Interactive: 4Ws Problem Canvas
Fill this out to practice scoping a new AI project.
Download: Problem Statement Template
After the 4Ws, you create a one-page summary. This template helps you structure your idea.
Open Problem Statement Template5.2 Data Acquisition
AI learns from data. This stage is about collecting the right information. We first identify features, which are the inputs the AI will use (e.g., for a weather-predicting AI, features would be temperature, humidity, wind speed).
Data can come from:
- Sensors: Like your phone’s camera, microphone, or GPS.
- Databases: Existing records from a company or school.
- Web Scraping: Collecting information from websites.
- Open Data Portals: Free, public datasets from governments or research (e.g., data.gov.in).
5.3 Data Exploration
Before we can use data, we must understand it. Data Exploration uses charts and statistics to find patterns, errors, and insights. This helps us clean the data (e.g., remove duplicates) and choose the right features. Basic charts are used to visualize the data.
Infographic: Common Data Visualization
Bar Chart
Used to compare quantities across different categories.
Pie Chart
Used to show the proportions (percentages) of a whole.
Scatter Plot
Used to find a relationship (correlation) between two variables.
The “Visualizing AI Bias” chart (in Section 3) is a good example of exploring data to find problems.
5.4 Modelling
This is where the “learning” happens. A model is a program that finds patterns in data. There are two main approaches:
| Modelling Approach | How it Works | Example |
|---|---|---|
| Rule-Based | Programmers write specific rules (e.g., “IF-THEN” statements). | A chatbot that gives the same 3 answers to a specific question. |
| Learning-Based | The model “learns” patterns from a large amount of data (Machine Learning). | A spam filter that learns to identify new types of junk email. |
5.5 Evaluation
How do we know if the model works? We test it. Evaluation uses a separate set of “testing data” that the model has not seen before. We measure its performance using different metrics.
- Accuracy: The percentage of correct predictions. (e.g., “It was right 90% of the time.”)
- Precision: Of all the times it predicted “Yes,” how often was it right? (Good for avoiding false positives).
- Recall: Of all the actual “Yes” cases, how many did it find? (Good for avoiding false negatives).
- F1-Score: A balance between Precision and Recall.
Infographic: Interactive Confusion Matrix
This table helps us see *where* our AI model is right or wrong. Hover over the 4 squares to learn more. (Example: A spam filter)
Title
Explanation goes here.
5.5.1 Choosing the Right Metric: Precision vs. Recall
Accuracy isn’t always the best metric. Sometimes, certain types of mistakes are much worse than others. This is where we choose between Precision and Recall.
Focus on Precision
Goal: To minimize False Positives.
Ask: “Of all the times the AI said ‘Yes’, how often was it right?”
Use Case: Email Spam Filter. A False Positive (a real email marked as spam) is very bad. We would rather let a little spam through (a False Negative) than lose an important email.
Focus on Recall
Goal: To minimize False Negatives.
Ask: “Of all the *actual* ‘Yes’ cases, how many did the AI find?”
Use Case: Medical Disease Scan. A False Negative (saying a sick patient is “healthy”) is extremely dangerous. We would rather have some False Positives (telling a healthy person to get more tests) than miss a real case.
5.5.2 Evaluation for Fairness
This is the most important cross-link. The Evaluation stage is not just about numbers like accuracy; it’s our chance to check for the AI Bias we learned about in Section 3.
Here, we must ask: “Is our model’s accuracy good for *all* groups?” We must test our model on data broken down by group.
For example, we would check the error rate for our facial recognition system (from the D3 chart) separately for Group A, Group B, and Group C. If we find the error rate for Group B is much higher (9.8%) than for Group C (3.1%), we have found bias. We must then go back to the Data stages to fix it, perhaps by collecting more data for Group B.
Cross-link: This shows how Evaluation links back to AI Ethics and can force us to repeat the Project Cycle to fix bias.
6. Q&A and Frequently Asked Questions
7. Key Chapter Notes
- Ethics is the study of what is right and wrong. AI ethics applies this to the decisions made by machines.
- Moral Dilemmas are “no-win” scenarios. The goal is not to find a “right” answer but to articulate the trade-offs of each choice.
- Data Privacy is about consent and control. Your data is valuable, and you have a right to know how it is used.
- AI Bias comes from biased data, not biased machines. It is a technical problem with serious social consequences.
- AI Access is an issue of fairness. Everyone should have the opportunity to benefit from and participate in the development of AI.
- AI Project Cycle: The 5 stages are Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation.
- Problem Scoping (4Ws): Defines *Who* has the problem, *What* it is, *Where* it happens, and *Why* it needs solving.
- Data Visualization: Using charts (Bar, Pie, Scatter) to understand data and find patterns.
- Modelling: Can be Rule-Based (explicit rules) or Learning-Based (learning from data).
- Evaluation: Uses metrics like Accuracy, Precision, and Recall to test the model.
- Precision vs. Recall: Precision minimizes false positives (e.g., spam filter). Recall minimizes false negatives (e.g., medical test).
- Fairness Metrics: Evaluation must also check if the model is biased against any group, linking to AI Ethics.
