Data can be a 'force for evil,' AI and machine learning experts say

The COVID-19 pandemic has highlighted and exacerbated existing disparities in the healthcare system, including the consequences of bias on racialized or marginalized groups. Some of the ways racial bias in the healthcare system emerge are more obvious, such as horror stories of Black people being turned away at emergency departments. Others, experts said during the HIMSS Machine Learning and AI for Healthcare Digital Summit this week, are less visible – but can still be incredibly harmful. "There are other ways this bias manifests structurally that are not as potentially sort of obvious," said Kadija Ferryman, industry assistant professor of ethics and engineering, NYU Tandon School of Engineering, at a panel on Tuesday. "That is through informatics and data."