AI Safety for Amazon: Building Responsible and Reliable Systems
Risk management across Amazon’s AI lifecycle
As Amazon expands AI across Alexa, AWS Bedrock, retail, and logistics, effective risk management is critical. Failures in fairness, accuracy, or security don’t just create inefficiencies — they can impact millions of customers, erode trust, and create regulatory or legal risks. Ensuring models are predictable, transparent, and aligned with ethical standards is essential to protect both end users and Amazon’s global brand.
AI safety safeguards against risks such as biased recommendations, hallucinated responses from Alexa, prompt injection in AWS applications, and vulnerabilities in logistics AI. Best practices span from adversarial testing to sourcing high-quality multilingual training data, ensuring systems operate with resilience, reliability, and fairness at Amazon scale.
Get the guide
With AI playing an increasingly prominent role across industries, safety measures have never been more important. This eBook explores a research-based approach to AI safety best practices across the AI lifecycle with examples highlighting AI safety in high-risk industries, such as law and medicine.
Download the eBook to learn:
- The most pressing risks in AI and strategies to mitigate them
- Best practices for AI safety across development, deployment, and application
- Challenges in ensuring AI fairness, transparency, and robustness
- The role of human oversight in improving AI safety and accountability
- How AI safety applies to high-risk industries like law, healthcare, and infrastructure
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