#cbse

Описание: #artificialintelligence#education#bias

IN this 40 minute Video You’ll Learn :

1. Bias Awareness

• What human bias means in simple language
• Everyday examples: Teacher bias, appearance bias, friend bias, Name bias
• How stereotypes influence our decisions
• Why AI “inherits” human biases through Data and Rules

2. Sources of Bias in AI

• Training Data Bias – Underrepresented groups, metro-only datasets, foreign accents
• Algorithm Design Bias – How developer assumptions shape results
• Prediction / Cognitive Bias – Human thinking patterns reflected in AI output
• Real examples from India: urban vs rural datasets, unequal representation

3. How AI Becomes Unfair (Real-World Failures)

• Facial Recognition Bias – Misidentification & wrongful arrests
• Predictive Policing Bias – Over-policing marginalized areas
• Hiring Algorithms – Favoring certain groups, reducing diversity
• Healthcare Bias – Unequal treatment based on demographics
• Credit Scoring Bias – Loan rejections based on socioeconomic status

Bias doesn’t just “happen.”
It perpetuates when AI keeps repeating old unfair human patterns.

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4. Mitigating AI Bias — How To Fix It

You’ll learn practical, classroom-ready explanations of:
• Using diverse datasets
• Detecting bias with tools & testing
• Designing fair algorithms
• Ensuring transparency in AI decisions
• Building inclusive AI development teams

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5. IBM AI Fairness 360 (AIF360)

A simple, teacher-friendly overview of:
• 70+ fairness metrics
• 10+ bias mitigation algorithms
• Pre-processing, prejudice remover, validation checks
• How developers ensure fairness and trust


Presented by Jagdish Chander, NIT Calicut alumnus, MBA,
--AI & STEM educator.

📍 Lab of Stupids Initiative

👉 Watch all Parts_1 and Part_2 and Part_3

💬 Drop your questions in the comments — let’s learn together.

📥 Free course material available via Google Drive (link in description).

https://drive.google.com/file/d/1XdUt...

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