Labeled data

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Labeled data is a group of

that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor.

Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data.

Crowdsourced labeled data

In 2006

Automated data labelling

After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data.^[2]

Data-driven bias

Algorithmic decision-making is subject to programmer-driven bias as well as data-driven bias. Training data that relies on bias labeled data will result in prejudices and omissions in a

References