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Bias and misinformation

Video Module 7: Ethical & Responsible Use of AI
  1. Algorithmic Bias:-Bias in AI isn't usually the result of "malicious" coding; rather, it’s a reflection of the data used to train the model. If the training data contains historical prejudices or lacks diversity, the AI will inevitably replicate those patterns.
    • Data Bias: If a hiring AI is trained on resumes from a company that historically hired only men, the AI may learn to penalize resumes containing the word "women’s" (e.g., "women’s chess club").
    • Representation Bias: Facial recognition systems often struggle with higher error rates for people with darker skin tones if the training datasets are predominantly composed of lighter-skinned individuals.
    • Confirmation Bias: AI recommendation engines can create "filter bubbles," showing users only content that aligns with their existing beliefs, which limits exposure to diverse perspectives.
  2. Misinformation and Hallucinations:- AI models are "probabilistic," not "database-driven." They predict the next most likely word or pixel, which can lead to the generation of false information presented with extreme confidence.
    • Hallucinations: An AI might invent a legal case, a historical date, or a scientific study that sounds perfectly plausible but is entirely fictional.
    • Deepfakes and Synthetic Media: AI can generate highly realistic images, audio, and video, making it difficult to discern reality from fabrication. This poses a massive threat to public trust and political stability.
    • Rapid Scaling: Unlike a human troll, an AI can generate thousands of unique, persuasive articles or social media posts in seconds, making it easy to flood the internet with "fake news."
  • Strategies for Responsible Use:-To navigate these issues, a framework of Responsible AI is necessary for both developers and users.

Pillar

Description

Transparency

Disclosing when content is AI-generated (e.g., watermarking) and explaining how models make decisions.

Accountability

Establishing who is responsible when an AI system causes harm or provides false information.

Inclusive Design

Using diverse datasets and involving a wide range of stakeholders during the development process.

Human-in-the-Loop

Ensuring critical decisions (medical, legal, financial) are reviewed by humans rather than fully automated.

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Course Content
Module 1: Introduction to AI & Prompt Engineering
Module 2: How AI Understands Language
How AI Predicts answer ( Simple Explanations)
Tokens, context and why AI forgets things
Why AI sometimes gives wrong or vague answers
Temperature & creativity ( Conceptual, not technical)
Module 2: How AI Understands Language (25 Questions)
25 Marks
Module 3: Core Prompt Writing Principles
Good prompt vs bad prompt
Common beginner mistakes
Prompt as instructions, not questions
Module 3: Core Prompt Writing Principles (25 Questions)
25 Marks
Module 4: Prompt structures & patterns
Instruction-based prompts
Role-based prompts (Act as)
Step-by-Step prompts
Output format prompts (List, Table, Emails)
Commonly used prompts
Module 4: Prompt Structures & Patterns (25 Questions)
25 Marks
Module 5: Prompting for everyday use cases
Students (notes, exams, explanations)
Professionals (emails, reports, presentations)
Content & marketing basics
Career & productivity use cases
Module 5: Prompting for everyday use cases (25 Questions)
25 Marks
Module 6: Improving & Debugging Prompts
Why prompts fail
Iteration & refinement
Prompt checklists
Asking AI to improve its own output
Module 6: Improving & Debugging Prompts (25 Questions)
25 Marks
Module 7: Ethical & Responsible Use of AI
AI hallucinations
Bias and misinformation
Academic integrity
Responsible usage guidelines
Module 7: Ethical & Responsible Use ofAI (25 Questions)
25 Marks

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