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Dataset bias

As explained in Natural Language Processing in Action, Second Edition

Dataset bias is systematic distortion in a dataset caused by how examples, labels, or populations are selected and represented.

Dataset bias is systematic distortion introduced when examples, labels, or populations are selected unevenly. A nationality classifier can inherit this distortion from scraped sources, broad language labels, and missing groups. section 8.2

Representation requires deliberate investigation: add data for underrepresented countries, inspect class coverage, and recognize that one surname may belong to several nationalities. Transliteration and reused spellings make the signal still less definitive. section 8.2

Inspecting a surname dataset

A surname dataset contains 37 nationality labels, including Moroccan, Arabic, Malaysian, and other categories. Sorting records, measuring unique-surname fractions, and examining overlaps reveals imbalance and conflicting labels before model training.

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Explains causes and demonstrates inspection, but stops short of prescribing a complete debiasing method.
Examples
Uses surname-nationality records, class counts, uniqueness ratios, duplicated names, and cross-nationality overlaps.
What it assumes
Assumes basic dataset handling and classification context while introducing representation and label-selection problems through examples.
What you can do after
Diagnose imbalance and label ambiguity by inspecting dataset contents before training.
How it connects
Connects dataset bias with representation, label quality, transliteration, and recurrent neural networks.
Cover of Natural Language Processing in Action, Second Edition
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