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The Ethics of AI

LESSON RESOURCES:

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https://coda.io/@peter-sigurdson/an-index-of-my-linked-in-blog-articles⁠

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Canadian Artificial Intelligence Association (CAIAC) coda.io⁠

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Canadian Artificial Intelligence Association (CAIAC) coda.io⁠

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Bayesian Models coda.io⁠

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“AI did it” is not tenable. Who is responsible for the decision execution of AI?

The answer to that question sets the real course for Ethical AI. Is it really AI, the super intelligent entity, that is doing it?

Is it AI, the nameless entity that will take control of the world? "Is AI really super intelligent?" is a separate question. We will parse it another time. Returning to the main point, it is great to see that there is a voice in the industry.

It is only going to get more and more people's attention.

This link can't be embedded.⁠

Glad to see sense in advocating that it is not AI that is doing it when the AI algorithm sends decisions for implementation, when in fact, behind the scene, the designer/analyst/db manager/data collector/data generator/data provider (tech team members) who are all part of creating an AI solution are responsible for the good/bad decisions in the implementation of AI - A position I have been advocating the AI community for long! Here it is explained by the distinguished Chief Decision Scientist from

#Google⁠

. This and more and more of such distinguished people’s positioning will help change the conversation for good about AI, where AI is commonly interpreted and attributed with uncontrolled runaway hypothesis as if nobody is responsible, as if the technology team is innocent! The Chief Decision Scientist says, "We have been complaisant and ... it is a dangerous reaction". This is the foundation for

#AI⁠

and

#aiethics⁠

Lesson Objective:

Students will gain a foundational understanding of the ethical issues related to Artificial Intelligence (AI) and its impact on society.

Topic 1: Introduction to AI and Ethical Considerations

Objective:

Introduce students to AI and its various applications.

How does AI work and how is it being used?

Highlight the importance of ethical considerations in AI development and deployment.

Activities:

Lecture: AI and its Impact on Society

Explain the basics of AI, machine learning, and deep learning.

Discuss the diverse applications of AI in different fields like healthcare, finance, and autonomous vehicles.

Present real-world examples where AI has raised ethical concerns.

Group Discussion: Ethical Dilemmas in AI

Divide the class into small groups.

Provide each group with a specific AI application (e.g., facial recognition, predictive policing).

Ask them to discuss potential ethical dilemmas associated with that application.

Have each group present their findings and insights to the class.

Topic 2: Ethical Principles and Frameworks in AI

Objective:

Introduce students to ethical principles and frameworks relevant to AI.

Encourage critical thinking about the application of these principles in AI development and decision-making.

Activities:

Lecture: Ethical Principles in AI

Present and explain fundamental ethical principles such as fairness, transparency, privacy, accountability, and human-centric design.

Discuss how these principles apply to AI technologies and why they are important.

Case Study Analysis: Ethical AI Use Cases

Provide students with real-world case studies involving AI implementations.

Ask them to analyze these cases using the ethical principles learned in the lecture.

Encourage open discussions and debates about the ethical implications and potential alternative approaches.

Topic 3: Bias and Fairness in AI

Objective:

Explore the concept of bias in AI systems and its impact on fairness.

Examine strategies to address and mitigate bias in AI.

Activities:

Lecture: Bias in AI Systems

Define bias in AI and its sources (e.g., biased training data, algorithm design).

Discuss how bias can lead to unfair outcomes in areas like hiring, lending, and criminal justice.

Workshop: Detecting and Mitigating Bias

Introduce students to tools and techniques for detecting bias in AI models.

Guide them through practical exercises to identify and address bias in sample datasets.

Topic 4: AI and Privacy, Accountability, and Future Implications

Objective:

Explore the ethical considerations related to AI and privacy.

Discuss the role of accountability and responsibility in AI development and deployment.

Consider the future implications of AI on society and ethical challenges that may arise.

Activities:

Lecture: AI and Privacy

Explain the privacy concerns associated with AI and data collection.

Discuss the impact of AI on personal information and privacy rights.

Guest Speaker or Panel Discussion:

Invite an expert in AI ethics, privacy, or law to speak to the class or participate in a panel discussion.

Discuss their experiences and insights on AI ethics and future implications.

Final Project Presentations:

Look for ways to integrate discussions and impacts of AI into your Course Topics.

Have students present their frameworks, including their justifications and how they address potential ethical concerns.

Assessment:

Case study analysis on ethical AI use cases.

Conclusion:

By the end of this Lesson, students have a better understanding of the ethical considerations surrounding AI technologies.

You are now equipped to critically assess AI applications and contribute to the responsible development and deployment of AI systems in society.

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