Emergent Medical Data: Health Information Inferred by Artificial Intelligence
11 U.C. Irvine Law Review 995 (2021)
73 Pages Posted: 24 Mar 2020 Last revised: 14 May 2021
Date Written: March 14, 2020
Artificial intelligence can infer health data from people’s behavior even when their behavior has no apparent connection to their health. AI can monitor one’s location to track the spread of infectious disease, scrutinize retail purchases to identify pregnant customers, and analyze social media to predict who might attempt suicide. These feats are possible because in modern societies, people continuously interact with internet-enabled software and devices. Smartphones, wearables, and online platforms monitor people’s actions and produce digital traces, the electronic remnants of their behavior.
In their raw form, digital traces might not be very interesting or useful; one’s location, retail purchases, and internet browsing habits are relatively mundane data points. However, AI can enhance their value by transforming them into something more useful—emergent medical data. EMD is health information inferred by artificial intelligence from otherwise trivial digital traces.
This Article describes how EMD-based profiling is increasingly promoted as a solution to public health crises such as the COVID-19 pandemic, gun violence, and the opioid crisis. However, there is little evidence to show that EMD-based profiling works. Even worse, it can cause significant harm, and current privacy and data protection laws contain loopholes that allow public and private entities to mine EMD without people’s knowledge or consent.
After describing the risks and benefits of EMD mining and profiling. The Article proposes six different ways of conceptualizing these practices. It concludes with preliminary recommendations for effective regulation. Potential options include banning or restricting the collection of digital traces, regulating EMD mining algorithms, and restricting how EMD can be used once it is produced.
Keywords: artificial intelligence, AI, machine learning, public health, digital phenotyping, privacy, big data, mental health, predictive analytics, surveillance
JEL Classification: I1, I12, I14, I18, K10, K23, O15, O32, O33, O38
Suggested Citation: Suggested Citation