AI Ethics and Responsible Data Science

Basic understanding of AI and machine learning concepts, as well as foundational knowledge in data science.

Evaluation will be based on participation in class discussions, completion of case study analyses, and a final project where students will develop and propose ethical guidelines for a specific AI application.

This course is meticulously designed for Data Science students aiming to navigate the complex ethical landscape of artificial intelligence and machine learning. By participating in this course, students will gain a profound understanding of the ethical considerations and societal impacts related to the deployment of AI technologies. The curriculum will cover a wide array of topics including bias and fairness, privacy, transparency, accountability, and the environmental impact of AI.

Students will delve into real-world case studies, analyzing incidents where AI systems have led to unintended consequences, and exploring strategies to mitigate these issues. The course will also examine the role of regulatory frameworks and guidelines in shaping ethical AI practices, with a particular focus on the most up-to-date policies and initiatives globally.

Interactive discussions and hands-on activities will encourage students to critically evaluate AI systems, fostering a sense of responsibility and ethical awareness in their future data science endeavors. By the end of this course, students will be equipped with the necessary tools to implement AI technologies ethically and responsibly, ensuring their work contributes positively to society.

The course emphasizes the importance of multidisciplinary collaboration, highlighting how ethics in AI requires input from diverse fields including philosophy, sociology, law, and computer science. Students will be encouraged to develop a holistic view of AI ethics, preparing them to be thoughtful and ethical leaders in the rapidly evolving field of data science.

By engaging with the most pressing ethical challenges in AI today, students will leave this course with a robust foundation in AI ethics, ready to navigate the complexities of the data science field with integrity and responsibility.

1. Ethics and Data Science by Mike Loukides, Hilary Mason, and DJ Patil

2. The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth

3. Journal of Artificial Intelligence and Ethics. Selected papers

4. AI Ethics Guidelines Global Inventory (AlgorithmWatch)