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.
Understanding AI Ethics
AI ethics encompasses the moral frameworks and practical guidelines that govern how artificial intelligence systems are designed, deployed, and regulated across society. Key concerns include bias in AI, where training data or algorithmic design leads to discriminatory outcomes against marginalized groups; transparency, where opaque models make decisions without explainable reasoning; and privacy, where AI systems process vast amounts of personal data. Real-world ethical failures have included facial recognition systems with higher error rates for darker-skinned individuals and hiring algorithms that penalized female applicants. The field also grapples with questions about autonomous systems making life-and-death decisions, deepfake proliferation, and the economic displacement caused by automation. AI ethics informs governance frameworks worldwide, from the EU AI Act to corporate responsible AI policies, and is deeply intertwined with AI alignment and AI safety research aimed at ensuring AI benefits humanity broadly.
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 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.
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.