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.
Understanding AI Safety
AI safety is the multidisciplinary field dedicated to ensuring that AI systems behave reliably, predictably, and beneficially throughout their lifecycle, from development through deployment and beyond. The field addresses near-term risks such as adversarial attacks that fool models into dangerous misclassifications, data poisoning that corrupts training pipelines, and reward hacking in reinforcement learning where agents find unintended shortcuts. It also tackles long-term concerns related to artificial general intelligence and artificial superintelligence, where misaligned goals could have catastrophic consequences. Practical AI safety work includes red-teaming large language models to discover harmful outputs, developing constitutional AI approaches for self-correction, and building interpretability tools that let researchers understand model internals. Organizations like Anthropic, DeepMind, and the Center for AI Safety have made safety a central research priority, recognizing that the power of AI systems must be matched by robust safeguards.
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.
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.
Explainable AI
Explainable AI (XAI) encompasses techniques that make AI system decisions understandable to humans. XAI is crucial for building trust, meeting regulatory requirements, and debugging model behavior.