Data · dataset · 2020
Thinking inside the box: Safe-by-Design for Responsible Learning about Emerging Biotechnologies
Listed in DANS Data Station Social Sciences and Humanities
Genetic engineering techniques (e.g., CRISPR-Cas) have led to an increase in biotechnological developments, possibly leading to uncertain risks.
Description
The European Union aims to anticipate these by embedding the Precautionary Principle in its regulation for risk management. This principle revolves around taking preventive action in the face of uncertainty and provides guidelines to take precautionary measures when dealing with important values such as health or environmental safety.
However, when dealing with ‘new’ technologies, it can be hard for risk managers to estimate the societal or environmental consequences of a biotechnology that might arise once introduced or embedded in society due to that these sometimes do not comply with the established norms within risk assessment. When there is insufficient knowledge, stakeholders active in early developmental stages (e.g., researchers) could provide necessary knowledge by conducting research specifically devoted to what these unknown risks could entail.
Read the rest (2 more)
In theory, the Safe-by-Design (SbD) approach could enable such a controlled learning environment to gradually identify what these uncertain risks are. In this paper, we present a conceptual design space for such an environment to which we refer as responsible learning. To enable such, we argue that three conditions need to be present: (1) co-responsibility between researchers and regulators, (2) some degree of regulatory flexibility, and (3) openness towards all stakeholders.
If one of these conditions would not be present, the SbD approach cannot be implemented to its fullest potential, thereby limiting an environment for responsible learning and possibly leaving current policy behind to anticipate uncertain risks.
Links
Where it is published
- Dataverse dataset page ssh.datastations.nl/dataset.xhtml?persistentId=doi%3A10.17026%2FDANS-X9U-G6U4 ↗
landing page · from ssh datastations nl
- DOI doi.org/10.17026/dans-x9u-g6u4 ↗
DOI / persistent id · from ssh datastations nl
Catalogue records · 1
- Dataverse API ssh.datastations.nl/api/datasets/:persistentId/?persistentId=doi%3A10.17026%2FDANS… ↗
metadata API · from ssh datastations nl
Topics
- Stated by source
- Arts and Humanities
- From keywords
- Humanities · Social Science
- Inferred from text
- Applied ethics 68%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DANS Data Station Social Sciences and Humanities | doi:10.17026/DANS-X9U-G6U4 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:5001 | enrichment · ssh datastations nl | taxonomy-embedding@1.0.0 | title+keywords+description (68%) |
| concepts[field].dataverse_subject:arts-and-humanities | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | /subjects |
| created_date | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | |
| description | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | /description |
| publication_date | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | |
| title | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | /name |
| updated_date | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 | |
| version_label | source · ssh datastations nl | connector:ssh_datastations_nl@1.0.0 |