Perera, A., Aleti, A., Tantithamthavorn, C. et al. Search-based fairness testing for regression-based machine learning systems. Empir Software Eng 27, 79 (2022). https://doi.org/10.1007/s10664-022-10116-7
Search-based fairness testing for regression-based machine learning systems
|Author:||Perera, Anjana1; Aleti, Aldeida1; Tantithamthavorn, Chakkrit1;|
1Faculty of Information Technology, Monash University, Melbourne, Australia
2Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
3Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
|Online Access:||PDF Full Text (PDF, 2.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022042530207
|Publish Date:|| 2022-04-25
Context: Machine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems.
Method: We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software.
Results: We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches.
Conclusion: These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it.
Empirical software engineering
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
3121 General medicine, internal medicine and other clinical medicine
Open Access funding provided by University of Oulu including Oulu University Hospital. The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support. Chakkrit Tantithamthavorn was partially supported by the Australian Research Council’s Discovery Early Career Researcher Award (DECRA) funding scheme (DE200100941).
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