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Example: AI models that help monitor air pollution or manage renewable energy contribute to a cleaner, greener
environment.
By focusing on sustainability, AI ensures long-term positive outcomes for communities and future generations.

VIDEO SESSION Century #Ethical & Moral Reasoning
21 st
Skills
Scan the QR code or visit the following link to watch the video:
AI FOR GOOD - Ethics in AI
https://www.youtube.com/watch?v=vgUWKXVvO9Q
After watching the video, answer the following question:

Elaborate on the statement: “We need to choose how we use AI, or else AI will choose how to use us.”

AI REBOOT

1. Fill in the blanks:
a. Ethical AI promotes fairness and .
b. Machines learn ethics and bias from .
2. Name any two principles of AI ethics codes.

Bias, Bias Awareness, AI Bias and Sources of Bias

A facial recognition algorithm might find it easier to identify a white person compared to a dark complexion person
due to the prevalence of white faces in the training data. This discrepancy can unfairly impact individuals from distinct
groups, reinforcing inequality and oppression. The challenge lies in the unintentional nature of these biases, which often
go unnoticed until they manifest in the software.

Bias
Bias is defined as prejudice against individuals or groups, especially in
ways that are considered unfair. “Bias in AI” has long been a key area
of research and attention in the machine learning community. It refers
to situations where ML-based data analysis systems are biased against
certain groups of people. These biases usually reflect the prevailing social
biases related to race, gender, biological sex, age, and culture. AI systems
learn to make decisions based on trained data, which may include biased
human decisions or reflect historical or social inequities.

Bias Awareness

In today’s connected world, AI technologies are becoming more important in different areas of our lives, such as
healthcare, finance, and criminal justice. However, as AI systems become more common, it’s crucial to recognise and
address the biases they may have.

Ethical Practices in AI 267