FairCompass: Operationalising Fairness in Machine Learning
Jessica Liu Huaming Chen Jun Shen Kim-Kwang Raymond Choo
As artificial intelligence (AI) increasingly becomes an integral part of our
societal and individual activities, there is a growing imperative to develop
responsible AI solutions. Despite a diverse assortment of machine learning
fairness solutions is proposed in the literature, there is reportedly a lack of
practical implementation of these tools in real-world applications. Industry
experts have participated in thorough discussions on the challenges associated
with operationalising fairness in the development of machine learning-empowered
solutions, in which a shift toward human-centred approaches is promptly
advocated to mitigate the limitations of existing techniques. In this work, we
propose a human-in-the-loop approach for fairness auditing, presenting a mixed
visual analytical system (hereafter referred to as 'FairCompass'), which
integrates both subgroup discovery technique and the decision tree-based schema
for end users. Moreover, we innovatively integrate an Exploration, Guidance and
Informed Analysis loop, to facilitate the use of the Knowledge Generation Model
for Visual Analytics in FairCompass. We evaluate the effectiveness of
FairCompass for fairness auditing in a real-world scenario, and the findings
demonstrate the system's potential for real-world deployability. We anticipate
this work will address the current gaps in research for fairness and facilitate
the operationalisation of fairness in machine learning systems.