Unit 8: AI Ethics and ValuesFive Pillars of AI Ethics, Algorithmic Bias, Policies & Moral Machine
The comprehensive CBSE Class 11 guide for Unit 8. Master the Five Pillars of AI Ethics, sources of bias (training data, algorithmic, cognitive), mitigation toolkits (IBM AI Fairness 360), corporate governance, autonomous vehicle ethical dilemmas, and 10 official handbook MCQs.
1. The Five Pillars of AI Ethics
1. Explainability
AI systems must not function as impenetrable black boxes. Stakeholders, users, and regulators must be able to understand why and how an algorithmic decision was reached.
2. Fairness
Models must treat all individuals and demographic groups equitably, eliminating systemic discrimination based on race, gender, disability, age, or income.
3. Robustness
Systems must operate dependably, reliably, and safely under changing real-world conditions without crashing, hallucinating, or behaving unpredictably.
4. Transparency
Organizations must disclose the design, operational assumptions, data sources, and known limitations of their AI deployments to enable public scrutiny.
5. Privacy
Personal information must be protected through strong encryption, anonymization, and informed consent, respecting personal autonomy and data dignity.
2. Sources of AI Bias & Real-World Case Studies
Unbalanced or skewed training datasets that over-represent or exclude specific demographic populations.
Flawed objective functions or developer biases that unfairly weight attributes (e.g. zip code or income).
Human mental shortcuts and cultural blind spots reflected in the problem framing and dataset collection.
- Healthcare Spend Bias (US 2018): An algorithm allocating clinical care to 200M patients assigned lower risk scores to Black patients because it used past healthcare spending as a proxy for illness severity, reflecting economic disparities rather than medical need.
- Facial Recognition Misidentification: Security algorithms exhibiting significantly higher error and false-positive rates for individuals of color and women due to training sets dominated by lighter skin tones.
- Hiring Tool Bias: Amazon's discontinued recruiting algorithm that systematically penalized resumes featuring the word "women's" because it was trained on 10 years of male-dominated tech hiring data.
3. Bias Mitigation & The MIT Moral Machine
IBM AI Fairness 360 (AIF360):
An industry-standard open-source toolkit providing 70+ fairness metrics to detect bias in models and 10+ mitigation algorithms (e.g. Optimized Preprocessing, Disparate Impact Remover, and Prejudice Remover) across preprocessing, in-processing, and post-processing stages.
The MIT Moral Machine & Autonomous Vehicle Dilemmas
When an autonomous vehicle experiences sudden brake failure, should the AI stay the course (hitting pedestrians crossing on red) or swerve into a barrier (endangering passengers or cyclists on the side)? The Moral Machine demonstrates that algorithmic governance requires philosophical consensus on the valuation of human life, risk assessment, and legal obligations.
Official Handbook MCQs & Answer Key
Click any option to instantly see if you're correct with the official CBSE explanation.
What is the primary focus of AI Ethics in modern computer science?
Which core ethical pillar is concerned with eliminating prejudice and discrimination from decision-making models?
What crucial role does Transparency play in responsible AI systems?
What represents a major ethical concern regarding AI and personal privacy?
How can unaddressed algorithmic bias in AI systems harm society?
Which of the following strategies is recommended for mitigating bias in AI systems?
What is the primary purpose of institutional AI ethical frameworks and policy guidelines?
Who shares responsibility for ensuring the safe and ethical deployment of AI systems?
Which of the following represents a positive application of AI assisting humanity highlighted in Unit 8?
What is the central role of AI ethics in the development of autonomous self-driving vehicles?
Unit 8 Quick Recall Cheat Sheet
Frequently Asked Questions (FAQ)
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