Three Laws Blog: Law and Artificial Intelligence

Colonna on AI in the Internet of Health Things @lianecolonna

Liane Colonna, Stockholm University Faculty of Law, has published Artificial Intelligence in the Internet of Health Things: Is the Solution to AI Privacy More AI? as Faculty of Law, Stockholm University Research Paper No. 90. 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.

Download the article from SSRN at the link.

Tschider on Preempting the Artificially Intelligent Machine @cybersimplesec

Charlotte Tschider, University of Nebraska, Lincoln, College of Law, has published Preempting the Artificially Intelligent Machine. Here is the abstract.

The medical device industry and new technology start-ups have dramatically increased investment in artificial intelligence (AI) applications, including diagnostic tools and AI-enabled devices. These technologies have been positioned as reducing climbing health costs while simultaneously improving health outcomes. Technologies like AI-enabled surgical robots, AI-enabled insulin pumps, and cancer detection applications hold tremendous promise, yet without appropriate oversight will likely pose major safety issues. While preventative safety measures may reduce risk to patients using these technologies, effective regulatory-tort regimes also permit recovery when preventative solutions are insufficient. The Food and Drug Administration (FDA), the administrative agency responsible overseeing the safety and efficacy of medical devices, has not effectively addressed AI system safety issues for its clearance processes. If the FDA cannot reasonably reduce the risk of injury for AI-enabled medical devices, injured patients should be able to rely on ex post recovery options, as in products liability cases. However, the Medical Device Amendments Act (MDA) of 1976 introduced an express preemption clause that the U.S. Supreme Court has interpreted to nearly foreclose liability claims, based almost completely on the comprehensiveness of FDA clearance review processes. At its inception, MDA preemption aimed to balance consumer interests in safe medical devices with efficient, consistent regulation to promote innovation and reduce costs. Although preemption remains an important mechanism for balancing injury risks with device availability, the introduction of AI software dramatically changes the risk profile for medical devices. Due to the inherent opacity and changeability of AI algorithms powering AI machines, it is nearly impossible to predict all potential safety hazards a faulty AI system might pose to patients. This article identifies key preemption issues for AI machines as they affect ex ante and ex post regulatory-tort allocation, including actual FDA review for parallel claims, bifurcation of software and device reviews, and dynamics of the technology itself that may enable plaintiffs to avoid preemption. This Author then recommends an alternative conception of the regulatory-tort allocation for AI machines that will create a more comprehensive and complementary safety and compensatory model.

Download the article from SSRN at the link.

Froomkin, Kerr, and Pineau on When AIs Outperform Doctors @iankerr @mfroomkin

A. Michael Froomkin, University of Miami School of Law, Ian R. Kerr, University of Ottawa Common Law Section, and Joelle Pineau, McGill University, have published When AIs Outperform Doctors: The Dangers of a Tort-Induced Over-Reliance on Machine Learning and What (Not) to Do About it. HEre is the abstract.

Someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. What will the dominance of ML diagnostics mean for medical malpractice law, for the future of medical service provision, for the demand for certain kinds of doctors, and—in the longer run—for the quality of medical diagnostics itself? This article argues that once ML diagnosticians, such as those based on neural networks, are shown to be superior, existing medical malpractice law will require superior ML-generated medical diagnostics as the standard of care in clinical settings. Further, unless implemented carefully, a physician’s duty to use ML systems in medical diagnostics could, paradoxically, undermine the very safety standard that malpractice law set out to achieve. In time, effective machine learning could create overwhelming legal and ethical pressure to delegate the diagnostic process to the machine. Ultimately, a similar dynamic might extend to treatment also. If we reach the point where the bulk of clinical outcomes collected in databases are ML-generated diagnoses, this may result in future decision scenarios that are not easily audited or understood by human doctors. Given the well-documented fact that treatment strategies are often not as effective when deployed in real clinical practice compared to preliminary evaluation, the lack of transparency introduced by the ML algorithms could lead to a decrease in quality of care. The article describes salient technical aspects of this scenario particularly as it relates to diagnosis and canvasses various possible technical and legal solutions that would allow us to avoid these unintended consequences of medical malpractice law. Ultimately, we suggest there is a strong case for altering existing medical liability rules in order to avoid a machine-only diagnostic regime. We argue that the appropriate revision to the standard of care requires the maintenance of meaningful participation by physicians in the loop.

Download the article from SSRN at the link.

Chung and Zink on the Liability of Artificial Intelligence In Medicine @leopardist

Jason Chung and Amanda Zink, both of New York University, are publishing Hey Watson, Can I Sue You for Malpractice? Examining the Liability of Artificial Intelligence in Medicine in the Asia-Pacific Journal of Health Law, Policy, and Ethics. Here is the abstract.

Currently, three South Korean medical institutions – Gachon University Gil Medical Center, Pusan National University Hospital and Konyang University Hospital - have implemented IBM’s Watson for Oncology artificial intelligence (AI) system. As IBM touts the Watson for Oncology AI’s to “[i]dentify, evaluate and compare treatment options” by understanding the longitudinal medical record and applying its training to each unique patient, questions regarding the status and liability of these AI machines have arisen. Given its ability to interpret data and present treatment options (along with relevant justifications), AI represents an interim step between a diagnostic tool and colleague in medical settings. Using philosophical and legal concepts, this article explores whether AI’s ability to adapt and learn means that it has the capacity to reason and whether this means that AI should be considered a legal person. Through this exploration, the authors conclude that medical AI such as Watson for Oncology should be given a unique legal status akin to personhood to reflect its current and potential role in the medical decision-making process. They analogize the role of IBM’s AI to those of medical residents and argue that liability for wrongful diagnoses should be generally based on a medical malpractice basis rather than through products liability or vicarious liability. Finally, they differentiate medical AI from AI used in other products, such as self-driving cars.

Download the article from SSRN at the link.

Tscheider on Deus ex Machina: Regulating Cybersecurity and AI for Patients of the Future @CybersimpleSec

Charlotte A. Tschider, Mitchell Hamline School of Law, is publishing Deus ex Machina: Regulating Cybersecurity and Artificial Intelligence for Patients of the Future in the Savannah Law Review (2018). Here is the abstract.

Translated as “god from a machine,” the deus ex machina mechanically suspended Greek gods above a theatre stage to resolve plot issues by divine intervention. The implement’s name has since extended to the rapidly expanding Artificial Intelligence (AI) field, as AI has similarly promised miraculous resolution to any number of human challenges, including chronic health conditions. Modern medical devices and other health applications use AI to automate computer functionality, including Internet-connected medical devices, ubiquitous mobile device use, and individual self-monitoring consumer health devices. Unlike human algorithmic programming of the past, AI enables more powerful and automated dynamic algorithmic calculation, or automation, which surpass human data science in accuracy. AI has the potential to revolutionize modern medicine, yet exceptionally large data volumes coupled with automated functionality and Internet connectivity will likely introduce previously unanticipated device safety issues. Although businesses increasingly integrate AI services into medical devices worldwide, the United States (U.S.) and the European Union (EU), typically global leaders in regulating health and medical device technology, have not established legal frameworks to adequately addresses big data, cybersecurity, and AI risks. With new technology, the principles informing medical device risk management activities no longer effectively manage patient safety risks. The U.S. and the EU must consider alternative strategies for regulating medical devices that adequately anticipate cybersecurity and AI risks.

Download the article from SSRN at the link.

The Little Robot That Can

Meet the Calgarian (Calgarite?) robot that helps children get over their fear of the doctor. MEDi, a cute little bot, won the Most Innovative Startup prize at the Pediatrics 40 Conference, for its ability to help younger patients get over the stress that comes with medical procedures. It's made by RxRobots.

More here from Calgary Metro News.