Guidelines for Responsible AI

Responsible AI means designing, building and running AI systems so they deliver their benefits while keeping harm to people and the business as low as possible. AWS breaks this into a set of core dimensions, such as fairness, safety and veracity, that teams check throughout a project. Exam…

Amazon Bedrock Guardrails is AWS's main tool for putting responsible AI rules into a generative AI application. You configure a guardrail once, with safeguards for harmful content, off-limits topics, banned words, personal data and factual accuracy, and it checks both what users send and what the…

Choosing a model is also a responsibility decision. Bigger models use more energy and hardware every time they run, so picking a model that is larger than the job needs wastes resources and adds to carbon emissions. AWS's guidance is to ask whether AI is needed at all, choose the smallest model…

Generative AI can create legal exposure for the business that deploys it, not just for the model provider. The exam guide names five risks: intellectual property infringement claims, biased model outputs, loss of customer trust, risk to end users, and hallucinations. This lesson explains what each…

A model learns its view of the world from its training data, so a responsible AI system starts with a responsible dataset. This lesson covers the four dataset characteristics the exam names (inclusive, diverse, curated and balanced), what goes wrong when each one is missing, and how to spot the…

Bias and variance are the two main ways a model's predictions go wrong. Too much bias means the model is too simple and underfits. Too much variance means it has memorised its training data and overfits. This lesson shows how to read each from a scenario, what fixes each one, and why a model that…

Bias isn't something you check once and forget. It can hide in the labels, in how a model treats each group, and in how the world changes after launch. This lesson covers the practical checks the exam names (label quality analysis, subgroup analysis and human audits), how to keep watching a model…

Some models let you see exactly how they reach an answer, while others give you an answer with no visible reasoning behind it. This lesson explains the difference between transparent, explainable models and opaque ones, how transparency differs from explainability, and how teams add explanations…

Transparency only works if the right information is written down and easy to find. AWS offers several ways to document and check models: SageMaker Model Cards for models you build, AI Service Cards for AWS's own AI services, Bedrock evaluation jobs for evidence of how a model performs, and model…

The most accurate model isn't always the right choice, and sharing everything about a system isn't always the safest choice. This lesson covers two tradeoffs the exam expects you to weigh: interpretability against predictive performance, and transparency against safety. In both, the answer depends…

An explanation only helps if the person reading it can understand it and act on it. Human-centered design for explainable AI means designing an AI system around the people who use it and the people it affects: telling them they're dealing with AI, showing why it reached an answer and how sure it…