Ethics 101

Introduction to Computational Ethics


Dr. Garcia

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Course Overview

Introduction to Computational Ethics

Lesson 1: Understanding Ethics and Computation

  • Objective: Define ethics and its importance in computational contexts.
  • Content: Overview of ethics, key ethical theories (deontology, utilitarianism, virtue ethics), and their application to computation.

Lesson 2: Historical Context of Computational Ethics

  • Objective: Trace the development of ethical considerations in computing.
  • Content: Milestones in computational ethics, from early computer usage to contemporary issues, and the evolution of ethical guidelines.

Lesson 3: Data Privacy and Security

  • Objective: Explore ethical issues related to data privacy and security.
  • Content: Case studies on data breaches, ethical implications of data collection, storage, and sharing, and best practices for safeguarding data.

Lesson 4: Algorithmic Bias and Fairness

  • Objective: Identify and address bias in algorithms.
  • Content: Examples of algorithmic bias, methods for detecting and mitigating bias, and ensuring fairness in computational processes.

Lesson 5: Ethics of Artificial Intelligence

  • Objective: Examine ethical considerations specific to AI.
  • Content: Discussion on the ethical design, deployment, and impact of AI systems, including issues of autonomy, accountability, and transparency.

Lesson 6: Ethical Implications of Machine Learning

  • Objective: Analyze the ethical challenges in machine learning.
  • Content: Exploration of ethical concerns in training data, model transparency, and the societal impact of machine learning applications.

Lesson 7: Intellectual Property and Open Source Ethics

  • Objective: Understand the ethical issues surrounding intellectual property in software development.
  • Content: Examination of copyright laws, open source principles, and the ethical considerations of software licensing.

Lesson 8: Professional Ethics in Computing

  • Objective: Discuss the ethical responsibilities of computing professionals.
  • Content: Professional codes of ethics (e.g., ACM, IEEE), case studies on ethical dilemmas faced by computing professionals, and strategies for ethical decision-making.

Lesson 9: Cybersecurity Ethics

  • Objective: Investigate the ethical aspects of cybersecurity.
  • Content: Ethical hacking, the balance between security and privacy, and ethical considerations in cybersecurity practices and policies.

Lesson 10: Future Directions in Computational Ethics

  • Objective: Explore emerging ethical challenges in computation.
  • Content: Discussion on future trends in technology (e.g., quantum computing, IoT) and the evolving ethical landscape, preparing for new ethical dilemmas in the computational field.

Each lesson includes reading materials, case studies, discussion questions, and practical exercises to apply ethical principles in computational scenarios.

  • This course requires enrollment in Ethics 101 and Ethics 102
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  • Last Update May 21, 2024