Reducing Bias in AI-based Analysis of Visual Artworks
Zhuomin Zhang (1), Jia Li (1), David G. Stork (2),
Elizabeth C. Mansfield (1), John Russell (1),
Catherine Adams (1), and James Z. Wang (1)
(1) The Pennsylvania State University, University Park, PA 16802
(2) Independent Consultant, Portola Valley, CA 94028 USA
Empirical research in science and the humanities is vulnerable to bias
which, by definition, implies incorrect or misleading findings.
Artificial intelligence-based analysis of visual artworks is
vulnerable to bias in ways specific to the domain. Works of art belong
to a distinct cultural category that often prioritizes such
characteristics as hand-craftsmanship, uniqueness, originality, and
imaginative content; works of art are also responsive to diverse
social and cultural contexts. Ascertaining which features of an
artwork can be rightly ascribed to an objective “truth,” without which
the concept of bias is not even relevant, is itself challenging.
Incorporating expert knowledge into machine learning applications can
help reduce bias in final estimates. We review several sources of bias
that can occur across different stages of AI-based analysis, protocols
and best practices for reducing bias, and approaches to measuring
these biases. This systematic investigation of various types of bias
can help researchers better understand bias, become aware of practical
solutions, and ultimately cultivate the prudent adoption of AI-based
approaches to artwork analysis.
Full Paper in Color
On-line Info on the Research
Zhuomin Zhang, Jia Li, David G. Stork, Elizabeth C. Mansfield, John
Russell, Catherine Adams and James Z. Wang, ``Reducing Bias in
AI-based Analysis of Visual Artworks,'' IEEE BITS the Information
Theory Magazine, Special Issue on Information Processing in Arts and
Humanities, vol. 2, no. 1, pp. 36-48, 2022.
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December 10, 2022