Trustworthy AI
Trustworthy AI is an approach to building AI systems that are reliable, fair, transparent, privacy-preserving, and safe. It encompasses technical, ethical, and governance dimensions of AI development.
Understanding Trustworthy AI
Trustworthy AI encompasses the technical and governance frameworks that ensure artificial intelligence systems are reliable, safe, fair, transparent, and aligned with human values throughout their lifecycle. It extends beyond responsible AI principles to include verifiable properties like robustness to adversarial attacks, calibrated uncertainty estimation, and consistent behavior across diverse populations. The European Union's AI Act and similar regulatory frameworks worldwide codify trustworthy AI requirements into law, mandating risk assessments and compliance measures for high-stakes applications. Technical approaches include formal verification, comprehensive benchmark testing, bias auditing, and watermarking to ensure accountability. Trustworthy AI requires human-in-the-loop mechanisms for oversight, explainable model decisions, and transparent documentation of training data and model limitations. Building trust in AI systems is essential for adoption in sensitive domains like healthcare, finance, and criminal justice, where errors can have profound consequences on individuals and communities.
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.