In the years when I worked as an IRB reviewer, I would often identify far too complex participant-facing materials, but I wouldn't know where to start to simplify or clarify the content. I've since had the opportunity to focus on health literacy and how health literacy best practices can fundamentally change the way science and research are communicated to patients, participants, and their caregivers. Read more
Hopefully the pandemic is not here to stay, but even when it's gone, I suspect work from home for research administrators will stay in one form or another for many people. Because in the end, it's not just the HRPPs going virtual: the entire research administrative enterprise is in this virtual world with them. Read more
This month’s Research Ethics Roundup covers the reportage of the PHS Study of Untreated Syphilis, considering the ethics of research on insects, multiple perspectives on patient engagement in clinical trials, and the challenges of developing drugs for ultrarare diseases. Read more
We urge funders, study sponsors, researchers, ethicists, research leaders, accountants, and other stakeholders involved in decision-making regarding payments to Community Advisory Board (CAB) members to view CAB members as experiential experts whose time is valuable and whose contributions to science have long been undervalued. Valuing and paying CAB members can help operationalize trustworthiness, ethics, justice, equity, and fairness in the research process. The notion of CAB member involvement needs to be urgently reconsidered if we aim to work for full and representative inclusion and critical equity. 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
