AI Project Cycle & AI Ethics)
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Sustainability
1. What is 'sustainability'?Answer: Sustainability means maintaining or supporting systems and resources so they endure for future generations. It involves using resources responsibly so the planet can support people in the long term.
2. What are the three broad categories of Sustainable Development Goals (SDGs)?
Answer: Economy, Society, and Biosphere (environment).
3. What is a 'system' and what is 'systems thinking'?
Answer: A system is a group of interconnected components. Systems thinking studies how those components interact, creating behaviour that differs from parts alone. It helps understand complex problems by mapping relationships and feedback loops.
4. What is a 'system map'?
Answer: A system map is a visual diagram that shows elements of a system and how they are connected, often with arrows indicating causal relationships and loops that reveal feedback.
AI Project Cycle
5. What is a project?Answer: A project is a series of tasks or activities completed to reach a specific goal within given constraints like time and resources.
6. Define 'project cycle'.
Answer: A project cycle is a sequence of phases through which a project progresses, guiding planning, execution, evaluation, and closure.
7. List the six stages of the AI Project Cycle.
Answer: Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment.
8. What is 'Problem Scoping' and which tool does the handbook suggest using?
Answer: Problem Scoping is defining the project goal, stakeholders, and context; the handbook suggests using the 4Ws Problem Canvas (Who, What, Where, Why).
9. Name three types of data used in AI projects.
Answer: Textual (text), Numeric (tables, numbers), Visual (images and videos).
10. What is the difference between primary and secondary data sources?
Answer: Primary data are original data you collect specifically for your project (surveys, experiments, sensor readings). Secondary data are pre-existing datasets collected by others (government portals, public datasets, research data).
11. Give three examples of government/open data portals mentioned.
Answer: data.gov.in (India), data.gov.au (Australia), data.europa.eu (EU Open Data).
12. What is 'data exploration'?
Answer: Data exploration is analyzing and visualizing collected data using charts and graphs to discover patterns, trends, and insights that help decide modelling approaches.
13. What is an 'AI model'?
Answer: An AI model is a computational system trained on data to learn patterns and make predictions or decisions on new input.
14. Differentiate between 'Rule-Based AI' and 'Learning-Based AI'.
Answer: Rule-Based AI follows human-written rules (IF X THEN Y). Learning-Based AI learns patterns from data without explicit rules, e.g., machine learning classifiers trained on labeled examples.
15. What does 'Evaluation' mean in the AI Project Cycle?
Answer: Evaluation is testing and measuring model performance (accuracy, error, precision, recall, etc.) to pick and improve the best model.
16. What is 'Deployment' in an AI project?
Answer: Deployment is making the AI solution available to users—via mobile apps, websites, or integrated systems—so it can solve the real-world problem.
AI Ethics
17. Define 'AI Ethics'.Answer: AI Ethics is a set of moral principles guiding responsible development and use of AI systems, covering fairness, privacy, transparency, and societal impact.
18. List four principles of ethical AI mentioned in the handbook.
Answer: Human-centric, Unbiased, Data-Protective, Sustainable AI Solutions.
19. Name common ethical concerns associated with AI.
Answer: AI bias, privacy invasion, job displacement, misinformation (deepfakes/fake news).
20. What is the 4Ws problem statement template format?
Answer: Our [stakeholders] who has a problem that [issue] when/while [context]. An ideal solution would [benefit for them].
21. Explain the AI Project Cycle and the importance of each stage.
Answer: The AI Project Cycle includes six stages:
• Problem Scoping: Define the problem clearly, stakeholders, and objectives (using 4Ws). This ensures you build the right solution.
• Data Acquisition: Collect reliable data (primary or secondary). Good data quality is crucial for model performance.
• Data Exploration: Visualize and analyze data (charts, scatterplots, time series) to spot patterns, errors, missing values and choose features.
• Modeling: Select and train AI/ML models (rule-based or learning-based) to learn the relationship between inputs and outputs.
• Evaluation: Test models using validation/test sets and metrics (accuracy, precision, recall); tune and select the best model.
• Deployment: Integrate the model into applications (mobile/web) so users can apply the AI solution in real contexts.
Each stage reduces risk and ensures the final system is useful, accurate, and ethical.
22. Describe how you would use the 4Ws canvas to scope a problem on 'air pollution in a city' (brief example).
Answer: WHO: Residents, children, health workers, city authorities.
WHAT: Rising air pollution causing health issues and restricting outdoor activities.
WHERE: Urban areas and schools in the city.
WHY: Solving it could improve public health and quality of life; early warnings can reduce exposure.
An ideal solution: An AI-based air quality monitoring and alert system that predicts pollution spikes and advises preventive measures.
23. Explain primary vs secondary data with examples and describe when to prefer each.
Answer: Primary data are collected specifically for your project—e.g., class surveys for BMI, sensor readings from local air-quality monitors, images you annotate. Use primary data when project needs tailored, up-to-date, or labeled information.
Secondary data are pre-existing datasets—e.g., government open data portals, telescope data from space missions, research datasets. Use secondary data when it is high-quality, saves time, or when historical context is needed.
24. Give a short plan for the 'Exoplanet detection' use-case across the AI Project Cycle.
Answer: Problem Scoping: Detect Earth-like exoplanets from telescope light-curve data.
Data Acquisition: Gather time-series light-curve data from telescopes (public missions) and simulated transit datasets.
Data Exploration: Plot time-series, identify transit dips, compute statistics, and clean noise.
Modeling: Train classifiers or signal-detection models to differentiate transit vs non-transit events (ML/DL methods).
Evaluation: Measure detection accuracy, false positives/negatives; cross-validate.
Deployment: Provide a dashboard or web app for astronomers to upload light curves and receive predictions.
25. Discuss 'systems thinking' and 'system map' using the coffee production example from the handbook.
Answer: Systems thinking treats coffee production as interconnected steps: seedling growth, harvesting, processing, roasting, packaging. A system map links elements such as climate, labor, processing methods, and market demand. Changing one element (e.g., processing method) affects others (flavour, cost). Time delays (e.g., growth years) are shown by longer arrows. Effective interventions may involve changing relationships (e.g., improving supply chain) rather than elements alone.
26. Explain common AI biases and give classroom-level examples and ways to mitigate them.
Answer: Common biases arise when training data reflects historical or social prejudices: e.g., facial recognition performing worse on certain skin tones, search results assuming gendered roles. Classroom example: a dataset of job applicants that has few women may cause an AI resume screener to prefer male candidates. Mitigation: collect diverse data, audit models for fairness, use bias-detection tools, include human oversight, and test on subgroups.
27. The AI project cycle is a:
- a) Linear process
- b) Cyclical process
- c) One-time task
- d) Random activity
28. Which is NOT a type of data used in AI?
- · a) Textual
- · b) Numeric
- · c) Visual
- · d) Emotional
29. Primary data sources include:
- · a) Surveys you conduct
- · b) Government open data
- · c) Published research data
- · d) Commercial datasets you did not collect
30. Which of the following is an ethical concern in AI?
- · a) Bias
- · b) Privacy invasion
- · c) Job replacement
- · d) All of the above
31. Rule-Based AI works by:
- · a) Learning from data
- · b) Following human-defined rules
- · c) Using random choices
- · d) Evolving autonomously
32. Data Exploration commonly uses which visualisations?
- · a) Bar graphs
- · b) Pie charts
- · c) Scatter plots and time-series graphs
- · d) All of the above
Summary of Unit 1: AI Project Cycle & AI Ethics
Sustainability and Systems Thinking
Sustainability means using and maintaining resources responsibly, so they endure for future generations.SDGs (Sustainable Development Goals) fall into three categories — Economy, Society, and Biosphere.
Systems Thinking is understanding how parts of a system interact and affect each other.
System Map visually represents relationships and feedback between elements (e.g., Coffee Production System).
AI Project Cycle
The AI Project Cycle helps create AI solutions step-by-step in a cyclical process with six stages:Problem Scoping – Define the problem and goal using 4Ws (Who, What, Where, Why).
Data Acquisition – Collect required data (text, numeric, visual) from primary or secondary sources.
Primary Data: Collected firsthand (surveys, sensors).
Secondary Data: Existing datasets (e.g., data.gov.in, data.gov.au).
Data Exploration – Analyze and visualize data using bar graphs, pie charts, scatter plots, etc.
Modeling – Build and train AI models (Rule-Based or Learning-Based).
Evaluation – Test the model’s performance using metrics like accuracy and precision.
Deployment – Implement and share the AI solution through mobile apps or websites.
Examples
Coffee Production System: Demonstrates system interconnections — harvesting, roasting, and packaging affect each other.
Exoplanet Detection: Example project showing data acquisition, visualization, model building, and deployment.
Key Concepts
Rule-Based AI: Works on fixed “IF–THEN” rules.Learning-Based AI: Learns automatically from data (machine learning).
Data Types: Textual, Numeric, and Visual.
Visualization Tools: Graphs, charts, plots.
AI Ethics
Definition: Moral principles guiding responsible AI development and use.Core Principles:
Human-centric – prioritizes people’s well-being.
Unbiased – prevents discrimination in data and algorithms.
Data-Protective – ensures privacy and responsible data handling.
Sustainable – balances innovation with societal and environmental needs.
Common Ethical Issues: Bias, privacy invasion, job loss, misinformation.
Bias Example: Virtual assistants mostly having female voices → gender bias.
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