Glossary

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LIME(also: Local Interpretable Model-agnostic Explanations)

An explainable AI technique, introduced by Ribeiro et al. in 2016, that approximates any black-box model's behaviour around a single prediction by fitting a simple interpretable model (usually sparse linear regression) to perturbed versions of the input. The resulting feature…

LLM Self-Reflection(also: AI Self-Assessment, Model Self-Evaluation)

A technique in which a large language model is prompted to evaluate and critique its own output, identifying errors, gaps, or areas for improvement. In the context of accessibility, LLM self-reflection involves asking the model to assess whether the code or UI it generated meets…

LLM-as-Judge(also: LLM as a Judge, Model-as-Judge)

An evaluation methodology in which a large language model is prompted to assess the quality of some artifact — generated text, code, a UI, or a response from another model — according to a structured rubric. LLM-as-judge is attractive because it scales automated evaluation to…

Literacy Bias(also: Literacy bias of a metric)

In accessibility research methodology, a literacy bias describes the phenomenon where an evaluation metric systematically produces different scores for participants with different reading-literacy levels, independent of the characteristic being measured. For example,…

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