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Bias in AI systems are systematic distortions in data, models, or decision processes that can produce unfair or discriminatory outcomes. This concept explains root causes, common types (e.g. data, sampling, measurement bias) and practical approaches to detect and mitigate bias across data collection, model training and deployment.
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Bias in AI systems means systematic distortion that repeatedly treats certain people or groups unfairly.
The problem grew from human and institutional prejudice being carried into data, objectives, and technical processes. It is an interdisciplinary field; OECD and IBM provide principles and tools without a single originator.
Follow the path from data collection through objective and model to decision. Bias can arise, compound, or become visible at every station. Group comparisons and domain review reveal whether outcomes differ unfairly.
Unrepresentative or historically distorted data shape the model.
Measurable criteria express the equal treatment expected in context.
Decisions may burden groups differently even when a model appears neutral.
The concept supports AI selection, evaluation, and monitoring. Fairness depends on context; data review, testing, and accountable human recourse belong together.
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