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Robustness & Safety in AI
AI systems should be reliable, secure, and resilient to misuse or adversarial attacks.
Why Robustness & Safety Matter
As AI systems are deployed in critical domains—healthcare, transportation, finance, infrastructure—their reliability and security become paramount. Robustness ensures that AI performs as intended under a wide range of conditions, while safety protects against harm from errors, failures, or malicious manipulation.
Dimensions of Robustness & Safety
Reliability: AI should function correctly and consistently, even in the face of unexpected inputs or environmental changes.
Security: Systems must be protected against hacking, data breaches, and adversarial attacks that could alter behavior or leak sensitive information.
Resilience: AI should recover gracefully from failures and be designed to minimize the impact of errors or disruptions.
Adversarial Robustness: Defend against attempts to fool or manipulate AI with specially crafted inputs (e.g., adversarial examples in image recognition).
Safe Failures: When things go wrong, AI should fail in a way that avoids harm and allows for human intervention.
Approaches to Robustness & Safety
Test AI systems extensively under diverse and challenging scenarios.
Use adversarial training and regular security audits to identify vulnerabilities.
Implement monitoring and alerting to detect anomalies or unexpected behavior in real time.
Design for human-in-the-loop oversight and clear escalation paths for intervention.
Document known limitations, failure modes, and mitigation strategies.
Challenges
AI systems can be brittle and sensitive to small changes in input or environment.
Adversarial attacks are an active area of research, with new threats emerging regularly.
Balancing robustness with flexibility and adaptability can be difficult.
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