Ethics of AI & ML

Course details

The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have brought with them a myriad of ethical considerations. This course aims to provide students with a comprehensive understanding of the ethical implications, challenges, and responsibilities associated with developing and deploying AI and ML technologies.

Start Date
TBD
Duration
14-15 weeks
Prerequisites
A basic understanding of AI and ML concepts. No specific course prerequisites, but foundational knowledge of AI & ML is recommended.
Learning Objectives
  • Grasp the ethical, social, and legal implications of AI and ML.
  • Understand the potential biases in AI and ML algorithms and their societal impact.
  • Delve deep into real-world case studies showcasing ethical dilemmas.
  • Evaluate the frameworks and guidelines for ethical AI development.
  • Engage in discussions and debates on current ethical AI topics.
Key Features
  • Emphasis on real-world examples and case studies.
  • Guest lectures from industry experts in AI ethics.
  • Collaborative projects analyzing ethical dilemmas in AI applications.
Learning path
  • Unit 1: Introduction to Ethics in AI & ML (3)
  • Unit 2: Bias and Fairness (4)
  • Unit 3: Transparency and Accountability (4)
  • Unit 4: Privacy and Security (4)
  • Unit 5: Autonomy and Control (3)
  • Unit 6: AI for Social Good (4)
  • Unit 7: Future Ethical Considerations (4)
  • Unit 8: Ethical Frameworks and Guidelines (2)

Topics covered

  • Overview of AI Ethics
  • History of Ethical Considerations in Technology
  • Current AI Landscape and Ethical Implications

  • Understanding Algorithmic Bias
  • Sources of Bias in Data and Models
  • Measuring and Mitigating Bias
  • Case Studies on Algorithmic Discrimination

  • Importance of Model Explainability
  • Tools and Techniques for Interpretability
  • Accountability in AI Systems
  • Case Studies on Model Transparency

  • Data Privacy Concerns in AI
  • Differential Privacy and Federated Learning
  • AI in Cybersecurity and Potential Threats
  • Case Studies on AI Privacy Breaches

  • Autonomous AI Systems and Their Risks
  • Human-in-the-loop AI Models
  • Case Studies on Autonomous AI Failures

  • Positive Applications of AI
  • AI in Healthcare, Environment, and Social Services
  • Potential Pitfalls and Ethical Considerations
  • Case Studies on AI for Good

  • AI in Warfare and Lethal Autonomous Weapons
  • AI and Job Displacement Concerns
  • The Ethical Implications of AGI (Artificial General Intelligence)
  • Open Discussions on Future Ethical Scenarios

  • Overview of Current Ethical Frameworks for AI
  • The Road Ahead: Establishing Universal AI Ethics Standards

Grading:

  • Class Participation: 10%
  • Weekly Assignments and Case Analyses: 30%
  • Group Projects: 20%
  • Mid-Term Examination: 20%
  • Final Examination: 20%

Reading Material: A curated list of readings comprising seminal texts, contemporary research papers, and relevant articles on the ethics of AI and ML.

Assignments: Assignments will challenge students to analyze real-world AI ethical dilemmas and propose actionable solutions.

Faculty: A leading expert in the realm of AI ethics, with both academic and practical experience in the field

Teaching Instructors: TBD

Contact Us

E-mail

arun.reddy@globuslearn.com

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