Chapter 1: Information Technology and Society · Term 1
Lesson 1-4 explained: Ethical Issues with AI
If training data is biased, an AI can reproduce that bias; using AI responsibly means checking for bias, being able to explain decisions, and knowing who is accountable — guided by fairness, transparency, privacy protection, and accountability.
Lesson summary
Because AI now makes decisions that affect people, three concerns stand out: an AI can be unfair when it learns from biased data; it can put people's privacy at risk; and it can be difficult to understand, or to hold anyone answerable for, its decisions.
Takeaway: If training data is biased, an AI can reproduce that bias; using AI responsibly means checking for bias, being able to explain decisions, and knowing who is accountable — guided by fairness, transparency, privacy protection, and accountability.
Key terms
- Algorithmic bias
- Privacy
- Explainable AI (XAI)
- Responsibility
- Fairness
- Transparency
- Privacy protection
- Accountability
Textbook page: Arabic book 24 · English book 26
Video lesson
Chapter 1, Information Technology and Society, in 8 minutes
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Free questions on this lesson
From the textbook question bank. Think of the answer first, then open it.
1. What is the difference between "responsibility" and "accountability" in this lesson?
- There is no difference; both mean transparency
- Accountability is the system explaining why it decided; responsibility is protecting personal data
- Responsibility assigns duties to developer, operator and user; accountability names who is held to account
- Responsibility always has one fixed distribution that fits every context and system
Answer
Correct answer: Responsibility assigns duties to developer, operator and user; accountability names who is held to account
Explaining the decision is not accountability — that is XAI — and no single distribution of responsibility fits every context.
3. Explainable AI (XAI) and responsibility · page 28
2. What does using AI responsibly mean, as in the lesson?
- Using it for every decision with no human review
- Checking bias, explaining decisions, knowing who is accountable
- Collecting as much personal data as possible
- Hiding how it works so competitors cannot copy it
Answer
Correct answer: Checking bias, explaining decisions, knowing who is accountable
Using AI responsibly: checking for bias, being able to explain decisions, and knowing who is accountable.
Ethical issues with AI (Key fact) · page 27
3. What does the principle of "transparency" mean?
- Not using data for a new purpose without permission
- Rating applicants regardless of gender
- Identifying the party whose duty failed
- Showing the decision process and inner workings clearly
Answer
Correct answer: Showing the decision process and inner workings clearly
Transparency: showing the AI's decision-making process and inner workings clearly.
4. Basic Principles of AI Ethics · page 28
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