Overview of AI Ethics
AI Ethics
Real-World Examples of Ethical Concerns in AI
Bias and Fairness in AI
AI Bias
Table 6.1: Factors that contribute to biased AI
Reducing Bias and Promoting Fairness in AI Systems Diverse and representative data
Debiasing techniques
Explainability and transparency
Human-in-the-loop design
Ethical principles
Regular monitoring and evaluation
Evaluating user's feedback
Oversampling
Undersampling
Data Augmentation
The Problem of Moral Responsibility in AI
Transparency and Explainability in AI and the Black-Box Problem
Black-Box System
Methods for Enhancing the Transparency and Explainability of AI Models
Another technique for improving AI explainability such as decision trees and decision rules,
Value-Based Reasoning in AI Systems
These systems must be able to reason about the ethical implications of different investments,
AI and Environmental Impact
Potential risk or harm
Conclusion
Regulatory Frameworks and Industry Standards
Sustainable AI Development in the Kingdom of Saudi Arabia
The Kingdom of Saudi Arabia plans to use AI systems and technologies
International AI Ethics Guidelines
Read the sentences and tick True or False.
Describe how AI and automation might lead to job displacement.
Outline how biased training data can contribute to biased AI outcomes.
Define the black-box problem in AI systems.
Compare how AIsystems can have both positive and negative impact on the environment.
