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Bias in machine learning - what is it good for?

Hellström, Thomas (author)
Umeå universitet,Institutionen för datavetenskap
Dignum, Virginia, Professor (author)
Umeå universitet,Institutionen för datavetenskap
Bensch, Suna (author)
Umeå universitet,Institutionen för datavetenskap
 (creator_code:org_t)
RWTH Aachen University, 2020
2020
English.
In: NeHuAI 2020 : First International Workshop on New Foundations for Human-Centered AI. - : RWTH Aachen University. ; , s. 3-10
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • In public media as well as in scientific publications, the term bias is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning. In some cases, we suggest extensions and modifications to promote a clear terminology and completeness. The survey is followed by an analysis and discussion on how different types of biases are connected and depend on each other. We conclude that there is a complex relation between bias occurring in the machine learning pipeline that leads to a model, and the eventual bias of the model (which is typically related to social discrimination). The former bias may or may not influence the latter, in a sometimes bad, and sometime good way.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

bias
machine learning
taxonomy bias

Publication and Content Type

ref (subject category)
kon (subject category)

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