Artificial intelligence has progressed in the past decade at a remarkable pace. This progress has caused AI to become an indispensable part of humans' lives. As a result, we must be aware of AI models' limitations in every aspect of our life, from ethical decision-making to the threads that AI can cause, from any disinformation that AI can produce to bias and fairness in AI. AI ethics is an ethics subfield regarded as a subset of technological ethics that applies to robots and other artificially intelligent entities. It concerns how developers, suppliers, authorities, and operators must behave to reduce ethical risk, whether due to design, insufficient implementation, or deliberate technical misuse, that can arise from AI within society. In practice, the ethics of AI are essential because it ensures that the organizations' or entities' AI projects respect human dignity and do not hurt individuals in any way.
TechClass Ethics of AI online course will first review the philosophy of ethics and why ethical decision-making is hard. Then, it will gradually explore the ethical aspects of AI and discuss each of these aspects as an ethical problem, as well as suggest some ideas that can address these problems. By the end of this course, you become familiar with the concepts of disinformation, privacy, bias and fairness, accountability, and the impact of AI on these concepts.
Learning outcomes
- Get familiar with the hardness of ethical decision-making
- Get familiar with the general framework of AI ethics
- Understand why AI can be a double-edged sword when it comes to disinformation generation
- Learn about the impact of AI on detecting and generating disinformation
- Get familiar with deepfake, how it works, and its consequences
- Get familiar with the non-maleficence concept in AI
- Learn why and how security became an integral part of AI systems
- Get familiar with the concepts of privacy and surveillance as well as people's rights to privacy
- Get familiar Impact of AI on surveillance and the way we can stand against its threads
- Learn about the concept of fairness and different types of bias in AI
- Understand the differences between equality and equity
- Get familiar with the concept of accountability in AI
- Learn about the responsible AI framework and its key features
- Learn about how AI ethics can be used in practice
- Learn about transparency in AI
- Get familiar with some real-life examples of ethical issues
Table of contents
Chapter 1: Intro to Course
- 1.1. Welcome!
- 1.2. About TechClass Data Science Department
- 1.3. Learning Outcomes
- 1.4. Your Expectations, Goals, and Knowledge
- 1.5. Abbreviations
- 1.6. Copyright Notice
Chapter 2: Introduction
- 2.1. What is AI?
- 2.2. What is Ethics?
- 2.3. The Trolley Problem
- 2.4. What is AI Ethics?
- 2.5. AI Ethics Framework
- 2.6. Quiz
Chapter 3: Disinformation
- 3.1. AI and Disinformation
- 3.2. Deepfakes
- 3.3. How to Deal with AI-enabled Disinformation?
- 3.4. AI Helps to Detect Fake News
- 3.5. Quiz
Chapter 4: Non-maleficence
- 4.1.Definitions of Non-maleficence
- 4.2.Security and Safety
- 4.3.How AI Provides more Security and Safety
- 4.4.Quiz
Chapter 5: Privacy and Surveillance
- 5.1. Human Right to Privacy
- 5.2. AI and Threats to Privacy
- 5.3. Impact of AI on the Surveillance World
- 5.4. How to Overcome the Threats
- 5.5. Quiz
Chapter 6: Bias and Fairness
- 6.1. Definitions of Bias
- 6.2. Different Types of Bias in AI
- 6.3. Fairness
- 6.4. Fairness in AI and Machine Learning
- 6.5. Quiz
Chapter 7: Responsibility and Accountability
- 7.1. What is Accountability?
- 7.2. Algorithms and Accountability
- 7.3. Responsible AI
- 7.4. Quiz
Chapter 8: Transparency
- 8.1. Transparency in AI
- 8.2. Responsible AI
- 8.3. The Transparency Paradox in AI
- 8.4. Quiz
Chapter 9: Ethics of AI in Practice
- 9.1. Ethical Issues in AI Applications
- 9.2. Mass Unemployment
- 9.3. Ethics as Doing
- 9.4. Next Steps
Chapter 10: Final Tasks
- 10.1. Project
- 10.2. Self-study Essay
Chapter 11: Finishing the Course
- 11.1. What We Have Learned
- 11.2. Where to Go Next?
- 11.3. Your Opinion Matters
- 11.4. Congrats! You did it!
Brochure
