# Lesson Plan: Ethical Concerns of Artificial Intelligence
Category: AI, Tech & Digital Skills
Lesson 1: Introduction & Fundamentals
Objectives:
– Understand the basic ethical concerns surrounding artificial intelligence.
– Familiarize with key concepts and definitions.
Content:
– Definition and Overview: Ethical concerns in AI refer to the moral principles and standards guiding the design, development, and implementation of AI technologies. These concerns include issues such as bias, transparency, privacy, and accountability.
– Foundational Knowledge:
– Read the Harvard Gazette article on ethical concerns in AI: [Ethical concerns mount as AI takes bigger decision-making role](news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/)
– Watch the YouTube video: [AI and Ethics: Can Machines Make Moral Decisions?](https://www.youtube.com/watch?v=cczS0xUI3ME)
Key Takeaways:
– Understand the basic ethical issues associated with AI.
– Recognize the importance of ethical considerations in AI development.
Activity:
– Reflect on a recent AI application you’ve interacted with. Identify any potential ethical issues related to its use. Write a short paragraph on your thoughts.
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Lesson 2: Practical Application & Techniques
Objectives:
– Explore real-world examples of ethical challenges in AI.
– Identify techniques to address these challenges.
Content:
– Real-world Use Cases:
– Study cases where AI decision-making has posed ethical dilemmas.
– Read about AI accountability issues in the California Management Review article: [Critical Issues About A.I. Accountability Answered](cmr.berkeley.edu/2023/11/critical-issues-about-a-i-accountability-answered/)
– View the video: [The Ethics of AI: Who’s Responsible for Machine Decisions?](https://www.youtube.com/watch?v=79tC9UXFWyI)
Key Takeaways:
– Recognize AI’s impact on industries like healthcare, finance, and law.
– Identify techniques for minimizing bias and ensuring fairness in AI systems.
Activity:
– Choose a field (healthcare, finance, etc.) and research how ethical AI concerns manifest in that field. Prepare a brief report on your findings.
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Lesson 3: Advanced Insights & Mastery
Objectives:
– Deep dive into advanced ethical considerations in AI.
– Understand best practices for managing AI ethics.
Content:
– Expert Insights:
– Examine emerging ethical issues and management strategies in AI.
– Read the European Parliament’s study on AI ethics: [The ethics of artificial intelligence: Issues and initiatives](www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdf)
– Watch the TED talk: [AI Is Dangerous, but Not for the Reasons You Think](https://www.youtube.com/watch?v=eXdVDhOGqoE)
Key Takeaways:
– Gain insight into complex ethical dilemmas like AI in warfare and surveillance.
– Learn strategies for ethical AI design and future initiatives to address these issues.
Activity:
– Develop a set of ethical guidelines for an AI application of your choice. Consider aspects like privacy, transparency, and accountability.
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Assessment
1. What is a primary ethical concern with AI decision-making?
– A) Speed of processing
– B) Lack of creativity
– C) Bias in algorithms
– D) Cost of implementation
Answer: C) Bias in algorithms
*Explanation*: A primary concern is the potential for AI algorithms to perpetuate or even exacerbate bias if not properly managed.
2. Which field is particularly challenged by AI ethical issues?
– A) Agriculture
– B) Healthcare
– C) Hospitality
– D) Real Estate
Answer: B) Healthcare
*Explanation*: Healthcare faces substantial ethical concerns regarding patient data privacy and AI decision-making.
3. What does AI accountability refer to?
– A) Speed of AI adoption
– B) Human oversight in AI development
– C) Reporting AI improvements
– D) Responsibility when AI causes harm
Answer: D) Responsibility when AI causes harm
*Explanation*: AI accountability deals with determining who is responsible when AI systems make harmful decisions.
4. How can bias in AI systems be minimized?
– A) Increasing data volume
– B) Only using AI in non-critical applications
– C) Ensuring diverse and representative training data
– D) Limiting AI to technical roles
Answer: C) Ensuring diverse and representative training data
*Explanation*: Minimizing bias involves using diverse data to train AI systems, ensuring they make fair decisions.
