From Intersectional AI Toolkit

Intersectional AI A-to-Z[edit]

This glossary of terms for Intersectional AI A-to-Z is a great place to get started. By all means it's only one example of definitions for these complex ideas, and it is meant as an open invitation for conversations and amendments! These concepts show the complexity of the topic seen from multiple angles; yet it is so important to try to break down these concepts into plain language in order to offer more openings for folks to join these conversations. Please chime in, ask questions, help make these definitions better!

When defining and talking about AI we have to be cautious as many of the words that we use can be quite misleading. Common examples are learning, understanding, and intelligence. [...] – Elements of AI:

AI terms are easy to mix up. AI is a subset of the field of computer science. Within it, machine learning is the technique that is commonly used currently and which includes a variety of practices within it, like neural networks and deep learning, much of which is discussed here. Almost all of these make use of work from data science, a separate but highly related field.




Aartificial intelligenceartificial intelligence
Bbias & variancebias (implicit)
Cconfidence intervalcode of conduct
Ddata cleaningdata colonialism
Eexplainabilityethical AI
Ffeature extractionFLOSS
JJavascripttransformative justice
Kk-meansKimberlé Crenshaw
Lloss functionAda Lovelace
Mmachine learningmarginalization
Nneural networknonbinary
Ooverfitting & underfittingothering
PPython / patterns, params power
Qquantification queer OS
Rregression & classificationracialization
Ssupervised & unsupervised sustainability
Uuncertainty unknowability
Vvalues & variablesvalue
W(bag-of-)wordswhite supremacy
XX as inputxenofeminism
YY as output y
ZZ- zines

Other Glossaries, Inspiration, Resources[edit]