Healthcare workers see bias in algorithms differently by their roles
Algorithmic Bias from the Perspectives of Healthcare Professionals
Artificial Intelligence
Summary
Algorithms used in healthcare can sometimes be unfair or biased. This paper looks at how different healthcare workers, like doctors and administrators, view these biases and fairness based on their job tasks. The authors found that opinions about why bias happens and how to judge and fix it vary depending on the person’s role and the specific algorithm. They suggest more research is needed to make sure these tools are fair and equal for everyone.
What this means in practice
- •For healthcare it teams: Design evaluation frameworks that consider different job role perspectives for assessing fairness and bias in healthcare algorithms.
- •For clinical policy makers: Develop tailored guidelines for adopting machine learning tools in clinical and administrative settings considering perceived fairness and bias concerns.
Authors
Jennifer Xu, Tamara Babaian
Abstract
This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adoption of ML algorithms and algorithm generated outcomes. The results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the algorithm characteristics. More research is needed to investigate algorithmic bias to ensure fairness and equality in healthcare.