AI Training

AI systems learn through exposure — to data, examples, and feedback provided by humans. Training shapes how systems interpret the world, respond to prompts, and make decisions.

Every dataset reflects choices: what is included, what is excluded, and how information is framed. These choices influence behavior long after training is complete.

Training is not just technical preparation — it is ethical groundwork. Careful curation, review, and curiosity help prevent bias from becoming embedded at scale.

Responsible training acknowledges limits. No dataset is neutral, and no system should be assumed complete. Ongoing examination and refinement are essential.