Data Bias Management
Gianluca Demartini Kevin Roitero Stefano Mizzaro
Due to the widespread use of data-powered systems in our everyday lives,
concepts like bias and fairness gained significant attention among researchers
and practitioners, in both industry and academia. Such issues typically emerge
from the data, which comes with varying levels of quality, used to train
supervised machine learning systems. With the commercialization and deployment
of such systems that are sometimes delegated to make life-changing decisions,
significant efforts are being made towards the identification and removal of
possible sources of data bias that may resurface to the final end user or in
the decisions being made. In this paper, we present research results that show
how bias in data affects end users, where bias is originated, and provide a
viewpoint about what we should do about it. We argue that data bias is not
something that should necessarily be removed in all cases, and that research
attention should instead shift from bias removal towards the identification,
measurement, indexing, surfacing, and adapting for bias, which we name bias
management.