Multi-cellular engineered living systems: building a community around responsible research on emergence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ranging from miniaturized biological robots to organoids, multi-cellular engineered living systems (M-CELS) pose complex ethical and societal challenges. Some of these challenges, such as how to best distribute risks and benefits, are likely to arise in the development of any new technology. Other challenges arise specifically because of the particular characteristics of M-CELS. For example, as an engineered living system becomes increasingly complex, it may provoke societal debate about its moral considerability, perhaps necessitating protection from harm or recognition of positive moral and legal rights, particularly if derived from cells of human origin. The use of emergence-based principles in M-CELS development may also create unique challenges, making the technology difficult to fully control or predict in the laboratory as well as in applied medical or environmental settings. In response to these challenges, we argue that the M-CELS community has an obligation to systematically address the ethical and societal aspects of research and to seek input from and accountability to a broad range of stakeholders and publics. As a newly developing field, M-CELS has a significant opportunity to integrate ethically responsible norms and standards into its research and development practices from the start. With the aim of seizing this opportunity, we identify two general kinds of salient ethical issues arising from M-CELS research, and then present a set of commitments to and strategies for addressing these issues. If adopted, these commitments and strategies would help define M-CELS as not only an innovative field, but also as a model for responsible research and engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it