Robustness
Robustness in AI refers to a model's ability to maintain performance when faced with unexpected inputs, adversarial attacks, or distribution shifts. Building robust AI systems is essential for reliable real-world deployment.
Understanding Robustness
Robustness measures how well a machine learning model maintains its performance when faced with unexpected inputs, noisy data, or adversarial attacks. A robust model generalizes effectively beyond its training data, handling edge cases and distribution shifts without catastrophic failure. This property is essential in safety-critical applications such as autonomous driving, medical diagnosis, and fraud detection, where errors carry serious consequences. Techniques for improving robustness include adversarial training, data augmentation, ensemble methods, and regularization. Evaluating robustness often involves stress-testing models with corrupted or out-of-distribution samples. The field of AI safety places robustness alongside AI alignment and interpretability as a core requirement for trustworthy AI systems deployed in production environments.
Category
AI Ethics & Safety
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Adversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
Adversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
Bias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
Constitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
Deepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.