AI and Data Ethics

Introduction

In this course, students will explore the ethical complexities of information and communication technology, as well as of data science in general. Combining theoretical foundations from information ethics and real-world inquiry, students will build their ethical imaginations and skills for ethically collecting, storing, sharing and analyzing data derived from human subjects including data used in algorithms. To this end, the course will examine legal, policy, and ethical issues that arise throughout the full lifecycle of data science from collection, to storage, processing, analysis and use, including, privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Practically, using case studies, students will explore current applications of quantitative reasoning in organizations, algorithmic transparency, and unintended automation of discrimination via data that contains biases rooted in race, gender, class, and other characteristics.

Course content

• Overview of Ethical Issues in Data Driven Organizations
• Philosophical Foundations of Ethical Use of Data
• Philosophical Challenges of Thinking in Categories
• Elements and Functions of Regulation
• Research Ethics for Data Science
• Inequity, Inclusion and Accessibility
• Privacy and Surveillance
• Algorithmic Bias and Discrimination
• Intellectual Property and Appropriation
• Values in Design
• Data Protection Law and the Ethical Use of Analytics
• Anonymization and Informed Consent
• AI Fairness, Accountability and Transparency
• Ethnography of Data and Analytics
• Design Fiction, and Futurism