Robust AI vs Non-Robust Models

Developers should learn about Robust AI when building AI systems for critical domains like healthcare, autonomous vehicles, finance, or cybersecurity, where reliability and safety are paramount meets developers should learn about non-robust models to avoid deploying unreliable systems in production, such as in autonomous vehicles, fraud detection, or medical diagnostics, where failures can have serious consequences. Here's our take.

Robust AI

Nice Pick

Developers should learn about Robust AI when building AI systems for critical domains like healthcare, autonomous vehicles, finance, or cybersecurity, where reliability and safety are paramount

Pros

• It is essential for mitigating risks such as adversarial examples that can fool models, data drift over time, or biases that lead to unfair outcomes
• Related to: adversarial-machine-learning, model-validation

Cons

• Specific tradeoffs depend on your use case

Non-Robust Models

Developers should learn about non-robust models to avoid deploying unreliable systems in production, such as in autonomous vehicles, fraud detection, or medical diagnostics, where failures can have serious consequences

Pros

• Understanding this helps in designing robust models that handle adversarial attacks, data drift, and out-of-distribution samples, ensuring better performance and trustworthiness in applications like natural language processing or computer vision
• Related to: robust-machine-learning, adversarial-attacks

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Robust AI if: You want it is essential for mitigating risks such as adversarial examples that can fool models, data drift over time, or biases that lead to unfair outcomes and can live with specific tradeoffs depend on your use case.

Use Non-Robust Models if: You prioritize understanding this helps in designing robust models that handle adversarial attacks, data drift, and out-of-distribution samples, ensuring better performance and trustworthiness in applications like natural language processing or computer vision over what Robust AI offers.

The Bottom Line

Robust AI wins

Developers should learn about Robust AI when building AI systems for critical domains like healthcare, autonomous vehicles, finance, or cybersecurity, where reliability and safety are paramount