Data Ethics Fundamentals

Every dataset carries choices about who is counted, how they're represented, and what a model will do with them, and those choices have ethical weight. In this course, explore the questions that shape how we collect, use, and build models with data. As AI and machine learning spread across industries, discover why data ethics is essential to trust and privacy. Learn how established frameworks—deontology, utilitarianism, virtue ethics, and the ethics of belief—each offer a different lens on the same decision. Examine how bias enters through data collection and curation, and how biased recruitment tools can harm certain groups. Along the way, build the judgment to make stronger, more defensible ethical decisions and earn greater user trust. By the end of this course, you'll be prepared to identify ethical risks and reason through them using established frameworks. This intermediate-level course is an ideal fit for data scientists, analysts, ML practitioners, and anyone committed to responsible data.

Learning objectives
Define ethics and data ethics in a modern AI context.
Compare frameworks: deontology, utilitarianism, virtue ethics, and ethics of belief.
Apply each framework as a lens on a data-related decision.
Identify how bias enters data collection and curation.
Recognize sources of data misuse.
Reason through ethical risks to protect trust and privacy.