Although germline editing has been the subject of debate ever since the 1980s, it tended to be based rather on speculative assumptions until April 2015, when CRISPR/Cas9 technology was used to modify human embryos for the first time. This article combines knowledge about the technical and scientific state of the art, economic considerations, the legal framework and aspects of clinical reality. A scenario will be elaborated as a means of identifying key ethical implications of CRISPR/Cas9 genome editing in humans and (...) possible ways of dealing with them. Unlike most other discussions of CRISPR/Cas9 germline editing, which are generally based on deontological arguments, the focus in this case will be on a consequentialistic argument against certain applications of germline and somatic editing that takes not only the potential benefits and risks but also socioeconomic issues into consideration. The practical need for an indication catalogue, guidelines for clinical trials, and for funding of basic research will be pointed out. It will be argued that this need for regulatory action and discussion does not stem primarily from the fact that CRISPR/Cas9 germline editing is revolutionary in terms of its ethical implications and potential for human therapy, although this is the prevailing view in the current discussion. Understanding the value and interest dependency of arguments put forward by different stakeholders and learning from past debates related to similar technologies might prove a fruitful method of reaching judgments and decisions that come closer to a consensus upon which society as a whole can agree - which after all should be the true goal of an ethical debate and of bioethics. (shrink)

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...) and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)