Building trust into AI
Amazon Science
Amazon has built a comprehensive responsible AI pipeline across four stages of model development—pretraining, post-training, evaluation, and third-party monitoring—incorporating over 70 internal and external RAI tools and funding or publishing more than 500 research papers. The company teaches RAI principles during pretraining using curated datasets, optimizes policy adherence through reinforcement learning from human feedback, creates model-breaking datasets for evaluation, and partners with third-party experts to assess frontier risks like CBRN and cyberattack research. Amazon's approach aims to anticipate risks, teach models to handle ambiguity, and build systems that adapt to regulatory and social changes across different geographies and applications.
Why it matters
Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
