A Systematic Review on Human Roles, Solutions, and Methodological Approaches to Address Bias in AI

Abstract

People play a significant role in designing, developing, and employing artificial intelligence (AI) systems. They can consider contextual information beyond the scope of AI models, thereby influencing system outcomes. At the same time, people’s choices or biases can introduce problems into the systems. This paradoxical scenario, in which people can both introduce and contribute to relieving the inherited machine bias, demands comprehensive and multidisciplinary approaches involving informed human interventions to improve systems’ performances and reduce their biases. Researchers across various communities have investigated multifaceted methods to reduce and mitigate bias in AI systems. Regardless of the method, humans are always involved in the debiasing method in one way or another, emphasizing the importance of human intervention during AI systems development. In this systematic review, we analyzed 100 peer-reviewed publications from various human-computer interaction (HCI) and machine learning (ML) venues. We discuss their research efforts to minimize data bias and algorithmic bias from three angles. First, we present a comprehensive taxonomy of bias mitigation solutions, analyzing the research methodologies and standard benchmarks for evaluating these solutions, highlighting the human researcher’s role in developing and evaluating solutions to address bias. Next, we identify humans’ roles in alleviating biases and specify how, when, and where their involvement occurs within the AI lifecycle. Finally, we summarize the research focus and methodologies across research disciplines. Our review revealed that, while technical solutions are essential, addressing bias requires a broad perspective that integrates human oversight, ethical frameworks, and interdisciplinary collaboration.

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