Assessing Perceived Fairness from Machine Learning Developer's Perspective

• URL: http://arxiv.org/abs/2304.03745v1
• Date: Fri, 7 Apr 2023 17:30:37 GMT
• Title: Assessing Perceived Fairness from Machine Learning Developer's Perspective
• Authors: Anoop Mishra, Deepak Khazanchi
• Abstract summary: unfairness is triggered due to bias in the data, curation process, erroneous assumptions, and implicit bias rendered within the algorithmic development process. In particular, ML developers have not been the focus of research relating to perceived fairness. This paper performs an exploratory pilot study to assess the attributes of this construct using a systematic focus group of developers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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