We are finally witnessing increasing adoption of digital pathology in clinical practice. Pathologists are beginning to embrace and utilize artificial intelligence (AI) tools and ideas to augment routine pathology practice. All stakeholders in pathology need to fully understand the ethical frameworks required to develop, deploy, and maintain transparent, trustworthy, explainable, scalable, interoperable, and sustainable digital AI tools. Read more
Worries about "bad" downstream interpretations or applications of research are not new. It has always been the case that someone could use research findings for bad ends, intentionally or not. So why worry about downstream harms with AI research? Isn’t this just AI research exceptionalism? Read more
When we talk about AI research, we are mainly talking about research that seeks to develop tools that will replace human decision-making. The development of AI typically involves the collection and use of huge amounts of data to train an algorithm to make decisions or predictions within some domain. While there may be risks to people whose data are included in the large data sets used to train algorithms, the most salient and serious risk of harm in AI research is to those on whom the AI is applied in the real world. Read more
The data collected from mobile mental health apps has promising benefit toward the field of mental health. The risks are usually limited to breach of confidentiality and investigators mitigate those risks by implementing processes to protect access to the data. In studies involving algorithms and personalized medicine, the risks increase. So, how do IRBs determine when risks outweigh the benefits? Read more
The digitization of everyday life has led to an interesting phenomenon for research administrators; the ethical concerns that arise from secondary uses of large and open data now pose a greater challenge for the ethical management of research data than do the conventional challenges of primary data acquisition. As debates over consent forms give way to discussions of differential privacy, it is hard to ignore the new reality that the highest levels of risk and benefit to human participants in research may now arise from secondary data uses. What should research administrators and IRB members do to understand and manage the risks and benefits? Read more
