AI Project Cycle and Ethical Framework Notes – Class 10 AI (417)
AI Project Cycle and Ethical Framework Notes | Class 10 AI Quick Revision
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AI Project Cycle – Introduction
- The AI Project Cycle is a step-by-step process used to develop AI-based solutions to real-world problems.
- It ensures that AI projects are organised, efficient, and relevant to real-world needs.
- The AI project cycle has mainly 6 stages.
Stages of AI Project Cycle
Problem Scoping:
- First stage of the AI Project Cycle.
- We understand the problem and define the goal we aim to achieve.
- We identify the factors that may affect the problem.
Data Acquisition:
- Second stage of AI project Cycle
- It comes after problem scoping and before data exploration.
- Collect relevant data from various sources needed to solve the identified problem.
- Data forms the base of the project
Data Exploration:
- It is third stage of AI Project Cycle.
- Represent data using graphs, databases, flowcharts, maps
- Identify patterns in data and get it ready for building effective AI Model.
AI Project Cycle and Ethical Framework Notes | Class 10 AI Notes
Modelling:
- It is fourth stage of AI Project Cycle.
- Select suitable models based on patterns identified.
- Test selected models and choose the most efficient one.
- Develop algorithm around the selected model.
Evaluation:
- it is fifth stage of AI Project Cycle.
- Test model using new data and improve model based on results.
- It decides if the model is ready to use in real time or not.
Deployment:
- It is 6th stage of AI Project Cycle.
- Integrate AI solution into real-world environment
- Deliver value to users and stakeholders
AI Domains – Introduction
- AI domain is a specific field where AI is applied.
- Each AI domain works on a particular type of data.
- Based on the type of data used, AI is broadly categorised into three domains.
- Statistical Data (Data Science)
- Computer Vision
- Natural Language Processing
Statistical Data (Data Science)
- Statistical data refers to numerical and structured information.
- Deals with collecting, maintaining, and analysing large datasets.
- The extracted information helps in decision-making.
- Example:
- Price Comparison Websites
- Temperature prediction
- Traffic prediction
Computer Vision (CV)
- Enables machines to analyse and understand visual information
- Process includes image acquisition, screening, analysis, and extraction
- Input types: photos, videos, thermal/infrared images
- Objective: teach machines to learn from pixels
- Examples:
- Face recognition
- Self-driving car
- Augmented reality
- Medical imaging
Natural Language Processing (NLP)
- Enables interaction between humans and computers using natural language
- Extracts information from spoken and written language
- Objective: understand and interpret human language
- Examples:
- Machine Translation (Google Translate, Microsoft Translator)
- Email Filters (spam detection)
- Grammer prediction
- Search engine
AI Notes Class 10 | AI Project Cycle and Ethical Framework Notes
Ethical Frameworks for AI
Frameworks
- Step-by-step guides to solve problems
- Ensure organized, consistent, and structured problem-solving
Ethical Frameworks
- Combine ethics (right vs wrong) with frameworks for fair and responsible use.
- Help avoid unintended harm while making decisions and build trust
- Provide systematic approach to moral decision-making
Need for Ethical Frameworks in AI
- Avoid Bias and Discrimination: AI should not produce biased or discriminatory outcomes
- Ensure Transparency: AI systems should make decisions in a clear and understandable manner.
- Promote Ethical Decision-Making: Ethical frameworks help ensure morally acceptable AI choices.
- Prevent Harmful Outcomes: They help identify and avoid risks before AI systems are deployed.
- Ensure Safety and Social Good: Ethical AI should be safe and beneficial for society.
Factors Influencing AI Decisions
- Culture
- Religion
- Value of humans and non-humans
- Intuition and personal values
AI Notes Class 10 | AI Project Cycle and Ethical Framework Notes
Types of Ethical Frameworks
1. Sector-based Frameworks
- Designed for specific industries
- Example: Bioethics (Healthcare)
- Applied in healthcare, finance, education, transport, governance, etc.
2. Value-based Frameworks
- Based on moral principles and values
- Followings are types of Value-based Framework:
- Rights-based:
- Protects human rights and dignity
- Prevents discrimination by AI
- Utility-based:
- Maximizes overall benefit
- Minimizes harm for maximum people
- Virtue-based:
- Focuses on character and intentions
- Emphasizes honesty, compassion, integrity
Bioethics Framework
- Ethical framework for healthcare and life sciences
- Ensures ethical AI use in health-related fields
Principles of Bioethics
- Respect for Autonomy.
- Do not harm.
- Ensure maximum benefit for all.
- Give justice.
1. Respect for Autonomy
- Users should understand how AI works
- Training data and predictions should be accessible
- Transparency in decision-making
2. Do Not Harm
- Avoid harm to all groups
- Use fair and equitable datasets
- Prevent misallocation of healthcare resources
3. Maximum Benefit
- Aim beyond avoiding harm
- Follow clinical standards
- Use unbiased data reflecting all populations
4. Justice
- Fair distribution of benefits and burdens
- Address societal biases like racism and sexism
- Consider social determinants of healthcare
AI Project Cycle and Ethical Framework Notes | Class 10 AI Notes Code 417
Conclusion
- Ethical frameworks, especially bioethics, help prevent unintended AI consequences
- Ethical AI ensures fairness, transparency, and positive impact