  Fairness in machine learning (ML) applications is an important practice for
developers in research and industry. In ML applications, unfairness is
triggered due to bias in the data, curation process, erroneous assumptions, and
implicit bias rendered within the algorithmic development process. As ML
applications come into broader use developing fair ML applications is critical.
Literature suggests multiple views on how fairness in ML is described from the
users perspective and students as future developers. In particular, ML
developers have not been the focus of research relating to perceived fairness.
This paper reports on a pilot investigation of ML developers perception of
fairness. In describing the perception of fairness, the paper performs an
exploratory pilot study to assess the attributes of this construct using a
systematic focus group of developers. In the focus group, we asked participants
to discuss three questions- 1) What are the characteristics of fairness in ML?
2) What factors influence developers belief about the fairness of ML? and 3)
What practices and tools are utilized for fairness in ML development? The
findings of this exploratory work from the focus group show that to assess
fairness developers generally focus on the overall ML application design and
development, i.e., business-specific requirements, data collection,
pre-processing, in-processing, and post-processing. Thus, we conclude that the
procedural aspects of organizational justice theory can explain developers
perception of fairness. The findings of this study can be utilized further to
assist development teams in integrating fairness in the ML application
development lifecycle. It will also motivate ML developers and organizations to
develop best practices for assessing the fairness of ML-based applications.
