XAI
XAI (Explainable Artificial Intelligence) refers to methods and techniques that make AI decision-making processes transparent and interpretable to humans. XAI builds trust and enables accountability in AI systems.
Understanding XAI
XAI (Explainable Artificial Intelligence) is the field dedicated to making AI systems' decisions transparent, interpretable, and understandable to humans. As machine learning models grow increasingly complex, particularly deep neural networks with millions of parameters, understanding why a model produces a specific output becomes both more difficult and more important. XAI techniques include SHAP values, LIME (Local Interpretable Model-agnostic Explanations), attention visualization, feature importance analysis, and counterfactual explanations. Regulatory frameworks like the EU AI Act and GDPR's right to explanation are driving demand for explainable models in finance, healthcare, criminal justice, and insurance. XAI serves multiple stakeholders: data scientists debugging models, business leaders building trust, end users understanding decisions, and regulators verifying compliance. Balancing model performance with interpretability remains one of AI's central ongoing challenges.
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.