Bias and unfairness in machine learning models: a systematic literature review
This addresses the problem of algorithmic unfairness and bias in ML models for researchers and practitioners, but it is incremental as it reviews existing knowledge rather than proposing new solutions.
The study conducted a systematic literature review of 40 articles from 2017 to 2022 to examine bias and unfairness in machine learning models, identifying various detection and mitigation approaches, fairness metrics, and tools, and recommended further research to standardize techniques for ensuring impartiality.
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context.