Synthetic biology is a broad term covering multiple scientific methodologies, technologies, and practices. Pairing biology with engineering, synbio seeks to design and build biological systems, either through improving living cells by adding in new functions, or creating new structures by combining natural and synthetic components. As with all new technologies, synthetic biology raises a number of ethical considerations. In order to understand what these issues might be, and how they relate to those covered in ethics literature on synbio, we conducted (...) an interview study with practicing synthetic biologists affiliated with a synthetic biology centre in Australia. Scientists identified a range of ethical challenges germane to the field, including precarious employment, pressures from industry, gender inequity, and the negative effects of the hyping of synbio. These challenges differed markedly from those identified in the ethics literature, whose treatment of the harms and benefits of synbio remains largely speculative and abstract. In our discussion of the pragmatic, every day ethical issues synthetic biologists face, we illustrate how issues of waste or research integrity play pivotal roles in everything from lived experiences in the laboratory, to long-term research trajectories guiding the field. In a confirmation of the ethical relevance of our participant’s views on the field, we argue that the subjects they raise must be included in any ethical analysis of synbio as a field. (shrink)

Since the beginning of the current hype around Artificial Intelligence (AI), governments, research institutions, and the industry invited ethical, legal, and social sciences (ELS) scholars to research AI’s societal challenges from various disciplinary viewpoints and perspectives. This approach builds upon the tradition of supporting research on the societal aspects of emerging sciences and technologies, which started with the Ethical, Legal, and Social Implications (ELSI) Program in the Human Genome Project (HGP) in the early 1990s. However, although a diverse ELS research (...) community has formed since then, AI’s societal challenges came to be mostly understood under the narrow framing of ethics and disconnected from the insights and experiences of past ELS research. In this article, we make up for this gap and connect insights from past ELS researchers with current approaches to research the societal challenges of AI. We analyse and summarize the history of “ELS programs” (programs that emerged since the HGP to support ELS research in a given domain) as three distinct eras: a genomics era, a nano era, and an RRI era. Each of these eras comprises several achievements and challenges relevant to ELS programs in AI research, such as the setup of independent funding bodies, the engagement of the wider public in research practice, and the increasing importance of private actors. Based on these insights, we argue that AI research currently falls back on self-regulatory, less participatory, and industry-led approaches that trouble ELS programs’ past achievements and hinder opportunities to overcome the still-existing challenges. (shrink)