Is AI Fair? Bias, Data, and Responsibility
This stack invites students to examine AI as a system shaped by human choices and therefore open to scrutiny, rather than as a neutral tool. Beginning with the foundations of algorithmic bias, students move toward understanding how bias affects real-world applications, then confront its global consequences, especially for communities in the Global South who are rarely centered in AI development. From there, students turn to questions about how their own data is collected and used, before arriving at a capstone lesson that asks them to articulate the principles that should guide ethical AI use. Together, these five lessons build a progressive argument that recognizing bias is only the first step and that responsibility requires attention to whose voices are included, whose data is used, and what values guide design decisions. This stack is well suited for social studies, ethics, or interdisciplinary AI literacy courses.
- 5 lessons
