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 three largest concerns around the ethics of AI/ML in artificial intelligence human subject research are explainability, identifiability, and algorithmic bias. It is imperative that IRBs understand the AI/ML’s explainability in order to fulfill their responsibility of adequately assessing the risk-benefit ratio in AIHSR. Read more
PRIM&R is pleased to introduce the members of the Blog Squad for our 2021 SBER and AER Conferences. The Blog Squad is made up of PRIM&R members who will share their insights before, during, and after the conference. Stay tuned as they share their conference insights on Ampersand. Read more
