Glossary

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Data Representativeness(also: Dataset Representativeness, Demographic Representativeness)

The degree to which a dataset reflects the diversity of the population it is intended to serve, particularly across demographic dimensions such as age, gender, race, ethnicity, disability, and socioeconomic status. In AI and machine learning, unrepresentative training data leads…

Data Stewardship(also: Dataset Stewardship, Data Governance)

The responsible management of data throughout its lifecycle, including decisions about collection, storage, access, sharing, and disposal. In accessibility research, participatory data stewardship involves disabled data contributors in decisions about how their data is used,…

Dataset Bias(also: Training Data Bias, Data Representation Bias, Sampling Bias)

A systematic skew in the composition of training data used to build machine learning models, resulting in models that perform well for overrepresented groups but poorly for underrepresented ones. In accessibility contexts, dataset bias is a pervasive problem: activity…

Digitized Assessment(also: Digitised Assessment, Digital Hiring Assessment, Computer-Based Employment Assessment)

A computer-based hiring test used by employers to evaluate candidates' personality, cognition, skills, or judgement. Common formats include personality inventories, gamified cognitive tasks (balloon-inflating risk tests, Flanker attention tasks, arithmetic mini-games),…

Disability Stereotyping(also: Disability Stereotype, Ableist Stereotyping)

The attribution of fixed, oversimplified characteristics to individuals based on their disability status. In the context of AI and language models, disability stereotyping occurs when systems associate specific disabilities with particular traits — for example, linking autism…

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