AI Safety: Building Responsible and Reliable System
Risk management across the AI lifecycle
As AI adoption accelerates across industries, effective risk management is essential. With AI increasingly embedded in critical sectors— such as healthcare, law, and infrastructure—unintended failures can have far-reaching consequences. Ensuring models are predictable, fair, and aligned with ethical standards helps mitigate risks such as misinformation, security vulnerabilities, and biased decision-making.
AI safety is key to preventing risks such as model bias, hallucinations, security threats, and legal liabilities. Best practices span a range of techniques and strategies, from adversarial prompting to sourcing high-quality LLM training data, ensuring AI systems operate with reduced risk to businesses and end users, improved performance, and strong alignment with human values. From minimizing bias to enhancing data security, AI safety is a foundational priority for all who build, deploy, or rely on these systems.
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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