AIBias
AI bias refers to systematic errors in a machine learning model's outputs that unfairly favor or disadvantage particular groups or outcomes, typically arising from skewed training data, flawed problem framing, or biased human decisions embedded in the data pipeline.
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Glossary Terms(7)
Explainable AI (XAI)
Explainable AI (XAI) is a set of methods and practices for making the decisions and internal workings of machine learning models understandable to humans, so t…
Responsible AI
Responsible AI is the practice of designing, building, and deploying artificial intelligence systems in ways that are fair, safe, transparent, accountable, and…
AI Bias
AI bias refers to systematic errors in a machine learning model's outputs that unfairly favor or disadvantage particular groups or outcomes, typically arising…
AI Governance
AI governance is the set of policies, processes, roles, and controls that organizations and regulators put in place to ensure AI systems are developed and used…
Synthetic Data
Synthetic data is artificially generated data — produced by algorithms, simulations, or generative models rather than collected from real-world events — that i…
Data Augmentation
Data augmentation is the practice of artificially expanding a training dataset by applying transformations to existing examples — such as rotating images or pa…
Data Drift
Data drift is a change in the statistical distribution of a machine learning model's input data over time, which can degrade model performance even if the unde…