Liane Colonna (Stockholm University – Faculty of Law) has posted “Artificial Intelligence in the Internet of Health Things: Is the Solution to AI Privacy More AI?” on SSRN. Here is the abstract:

The emerging power of Artificial Intelligence (AI), driven by the exponential growth in computer processing and the digitization of things, has the capacity to bring unfathomable benefits to society. In particular, AI promises to reinvent modern healthcare through devices that can predict, comprehend, learn, and act in astonishing and novel ways. While AI has an enormous potential to produce societal benefits, it will not be a sustainable technology without developing solutions to safeguard privacy while processing ever-growing sets of sensitive data.

This paper considers the tension that exists between privacy and AI and examines how AI and privacy can coexist, enjoying the advantages that each can bring. Rejecting the idea that AI means the end of privacy, and taking a technoprogressive stance, the paper seeks to explore how AI can be actively used to protect individual privacy. It contributes to the literature by reconfiguring AI not as a source of threats and challenges, but rather as a phenomenon that has the potential to empower individuals to protect their privacy.

The first part of the paper sets forward a brief taxonomy of AI and clarifies its role in the Internet of Health Things (IoHT). It then addresses privacy concerns that arise in this context. Next, the paper shifts towards a discussion of Data Protection by Design, exploring how AI can be utilized to meet this standard and in turn preserve individual privacy and data protection rights in the IoHT. Finally, the paper presents a case study of how some are actively using AI to preserve privacy in the IoHT.

Daniel Schwarcz (University of Minnesota Law School) has posted “Health-Based Proxy Discrimination, Artificial Intelligence, and Big Data” (Houston Journal of Health Law and Policy, 2021) on SSRN. Here is the abstract:

Insurers and employers often have financial incentives to discriminate against people who are relatively likely to experience future healthcare costs. Numerous federal and states laws nonetheless seek to restrict such health-based discrimination. Examples include the Pregnancy Discrimination Act (PDA), the Americans with Disabilities Act (ADA), the Age Discrimination in Employment Act (ADEA), and the Genetic Information Non-Discrimination Act (GINA). But this Essay argues that these laws are incapable of reliably preventing health-based discrimination when employers or insurers rely on machine-learning AIs to inform their decision-making. At bottom, this is because machine-learning AIs are inherently structured to identify and rely upon proxies for traits that directly predict whatever “target variable” they are programmed to maximize. Because the future health status of employees and insureds is in fact directly predictive of innumerable facially neutral goals for employers and insurers respectively, machine-learning AIs will tend to produce similar results as intentional discrimination based on health-related factors. Although laws like the Affordable Care Act (ACA) can avoid this outcome by prohibiting all forms of discrimination that are not pre-approved, this approach is not broadly applicable. Complicating the issue even further, virtually all technical strategies for developing “fair algorithms” are not workable when it comes to health-based proxy discrimination, because health information is generally private and hence cannot be used to correct unwanted biases. The Essay nonetheless closes by suggesting a new strategy for combatting health-based proxy discrimination by AI: limiting firms’ capacity to program their AIs using target variables that have a strong possible link to health-related factors.

Recommended.