Alignment: Balancing Accuracy and Responsibility

In this lesson, we work with the concept of alignment, understood as the process of fine-tuning artificial intelligence model responses so they respect human values, ethical standards, and specific goals. In the educational sphere, alignment aims to ensure safe and appropriate responses for the school context. An unaligned model might provide risky instructions when given an seemingly innocent prompt, whereas an aligned model prioritizes safety and adapts its output to the student's environment. One of the most widely used techniques for aligning models is Reinforcement Learning from Human Feedback (RLHF), where human evaluators rate different model responses, which are then used to teach the system which types of answers to prioritize.

We also analyze the challenges of alignment. The "correct" response depends on the user's context, age, and culture. A model can fail if it ignores cultural differences, automatically rejects sensitive topics without contextualizing them, or answers from a perspective centered on another country. Cultural alignment requires systems to understand local knowledge and references—such as traditions, holidays, or specific vocabulary—rather than relying solely on a limited global subset. Without research and culturally diverse data, models tend to make errors or oversimplifications. Thus, advancing alignment is not just a technical challenge, but a cultural and pedagogical one as well.

At the end of the lesson, you are invited to answer a few questions. 

👩‍🏫 Instructor: Sofia Martinelli