SHAP
SHAP (SHapley Additive exPlanations) is an explainability method based on game theory that assigns each feature an importance value for a particular prediction. SHAP provides consistent, locally accurate explanations for any ML model.
Understanding SHAP
SHAP (SHapley Additive exPlanations) is an explainability framework rooted in cooperative game theory that assigns each feature an importance value for a particular prediction. Based on Shapley values from economics, SHAP provides mathematically grounded, locally accurate explanations for any machine learning model, from simple decision trees to complex deep learning networks. It answers the question of how much each input feature contributed to pushing a prediction away from the baseline average. SHAP is widely used in finance for credit scoring explanations, in healthcare for diagnostic model transparency, and in regulatory compliance where model interpretability is mandated. Its visualizations, including summary plots, dependence plots, and force plots, have become standard tools in the XAI (Explainable AI) toolkit.
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.