Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machine learning systems must be developed responsibly. Many high-level declarations of ethical principles have been put forth for this purpose, but there is a severe lack of technical guidelines explicating the practical consequences for medical machine learning. Similarly, there is currently considerable uncertainty regarding the exact regulatory requirements placed upon medical machine learning systems. This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges. First, a brief review of existing regulations affecting medical machine learning is provided, showing that properties such as safety, robustness, reliability, privacy, security, transparency, explainability, and nondiscrimination are all demanded already by existing law and regulations - albeit, in many cases, to an uncertain degree. Next, the key technical obstacles to achieving these desirable properties are discussed, as well as important techniques to overcome these obstacles in the medical context. We notice that distribution shift, spurious correlations, model underspecification, uncertainty quantification, and data scarcity represent severe challenges in the medical context. Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent models, comprehensive out-of-distribution model testing and verification, as well as algorithmic impact assessments

On the Road to Addiction: The Facilitative and Preventive Roles of Marketing Cues

This research broadens the focus on the addiction process by examining the role of marketing cues in the “pre-addiction” phase of the consumption continuum that is broadly conceptualized to include behavior that may or may not result in addiction. If addictive behavior is to occur then dependence on that behavior occurs leading to negative or harmful consequences as consumption increases over time becoming a critical component of the individual\u27s life. Of central interest to this research are the environmental triggers that influence such pre-addiction consumption behaviors with a specific focus on the role marketing cues can play in facilitating and preventing the progression from non-use to addiction. We suggest that marketing cues have the potential to heavily influence the path towards and away from addiction and we identify types of cues that can impact each phase, or multiple phases, of the consumption continuum