UNIT 1: Revisiting AI Project Cycle & Ethical Frameworks for AI

UNIT 1: Revisiting AI Project Cycle & Ethical Frameworks for AI 

Just like making a greeting card involves planning, AI projects also follow steps.
The AI Project Cycle has 6 stages:



  1. Problem Scoping

    • Identify the problem you want to solve.

  2. Data Acquisition

    • Collect data from reliable sources.

  3. Data Exploration

    • Represent data using graphs/charts and find patterns.

  4. Modelling

    • Choose and build an AI model.

  5. Evaluation

    • Test the model with new data and check its accuracy.

  6. Deployment

    • Use the model in the real world.

2. AI DOMAINS

AI works with different types of data.
Three major domains:



1. Statistical Data

  • Uses large amounts of numerical data.

  • Helps in decision-making.

  • Example: Price comparison websites (Shopzilla, PriceGrabber).

2. Computer Vision (CV)

  • Teaches machines to understand images & videos.

  • Examples:

    • Drones for agricultural monitoring

    • Surveillance systems

3. Natural Language Processing (NLP)

  • Helps computers understand human language.

  • Examples:

    • Email spam filters

    • Google Translate

3. FRAMEWORKS AND ETHICAL FRAMEWORKS

Framework

A step-by-step method to solve a problem.

Ethical Framework

A framework that helps us make decisions that are morally right and avoid harm.

Why AI Needs Ethical Frameworks?

  • To avoid bias (e.g., hiring algorithm biased against women).

  • To ensure AI makes fair, safe, and right decisions.

  • To prevent unintended consequences.

4. FACTORS THAT INFLUENCE DECISIONS

These can unknowingly affect how we judge situations:

  • Culture / Religion

  • Personal values

  • Bias towards familiar people

  • Location of the person affected

  • Hidden or missing information

5. TYPES OF ETHICAL FRAMEWORKS

There are two main types:



1. Sector-based Frameworks

Ethical rules for a specific field.
Examples:

  • Healthcare (Bioethics)

  • Finance

  • Education

  • Transportation

  • Law enforcement

2. Value-based Frameworks

These are based on moral values.

i. Rights-based

  • Protects human rights and dignity.

  • AI should not discriminate.

ii. Utility-based

  • Choose what gives the maximum benefit to most people.

iii. Virtue-based

  • Focus on good character qualities like honesty, compassion, fairness.

6. BIOETHICS (Healthcare Ethical Framework)

Used in medicine and life sciences.
Helps ensure that AI in healthcare is fair and safe.

Principles of Bioethics

  1. Autonomy

    • Respect patient's right to know and decide.

  2. Non-maleficence (Do No Harm)

    • Avoid causing harm.

  3. Beneficence (Maximum Benefit)

    • Must do good and improve patient well-being.

  4. Justice

    • Fair and equal treatment for everyone.

7. CASE STUDY SUMMARY (AI in Healthcare)

What happened?

  • AI was used to find high-risk patients.

  • It misjudged patients from the Western region.

  • These patients were actually sicker but received less care.

Why?

  • AI used healthcare expense data, not true medical conditions.

  • Training data was biased, since U.S. records spent less on certain groups.

How Bioethics Could Fix It?

  1. Autonomy:

    • Patients should know how the model works.

  2. Do Not Harm:

    • Use fair and unbiased data.

  3. Maximum Benefit:

    • Improve the model to help all regions fairly.

  4. Justice:

    • Ensure no group is disadvantaged.


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