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Statistical data plays a crucial role in AI, as it provides the foundation for AI systems to learn
from vast amounts of data, make predictions, and improve their accuracy. AI systems heavily

rely on their big data, but if the data is biased or incomplete, it can lead to unfair outcomes.
In computer vision, biases can result in misidentification based on a particular race or gender.
In NLP, it may cause errors in the interpretation of certain languages, slang, or dialects. The
ethical use of data ensures fairness, transparency, and accountability. This also includes

respecting privacy and ensuring that AI works equally well for all people.

Task 21 st Century #Social Interaction
Skills

1. Mock Parliament Session.

Set up a mock parliament session where the class will be divided into two groups
representing ruling party and opposition. The Ministry of Science and Technology will

lead the session analysing the ethical issues of implementation of AI in our country.
The two sides will debate for and against and the voting will take place at the end.
2. Divide the class into various small groups and have a debate on:

• possible bias in data collection.
• will it be fine to use AI when there is a possible threat of loosing jobs by many

unskilled workers?
• should income be shared by humans and machines when machines take place of

humans?
• AI is a boon or a curse?

At a Glance

• AI ethics integrates the principles that guide AI’s development and implementation, ensuring

its alignment with human values to benefit society.
• Computer Vision enables machines to see and interpret the visual world through images and
videos.
• To keep data safe, it should be encrypted, shared only with authorised individuals, and stored
in secure locations.
• AI systems depend on big data, but biased or incomplete data can lead to unfair outcomes. In
computer vision, biases may cause misidentification based on race or gender.

• Accountability means taking responsibility for your actions, especially when things go wrong.
• Statistical data on safety, like crime rates or accidents, can be biased if not collected fairly,
lacks key information, or is misinterpreted.

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