SIENNA D5.6: Recommendations for the enhancement of the existing legal frameworks for genomics, human enhancement, and AI and robotics
Open accessAbstract
This report identifies potential changes needed in the existing legal and human rights frameworks (international, EU and national) that might be necessary or desirable to create an environment in which the SIENNA proposals for ethical human genetics and genomics, human enhancement technologies and AI and robotics could be implemented most effectively. It also includes recommendations to improve enforcement and promote the uptake and effectiveness of the existing legislation in these fields. The desired or necessary changes advanced are specified in the report along with related actions, actors responsible for implementing them, their priority levels, implementation challenges and how these could be addressed. The report also discusses the interrelations between ethics and law from the perspective of policymakers.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the EU, the general caution to avoid creating new legal personality for AI systems has been echoed manifold.
“In the EU, at least, however the general caution to avoid creating new legal personality for AI systems has been echoed manifold ( Siemaszko, Rodrigues, Slokenberga, 2020 ;Bryson, Diamantis, and Grant 2017 )”
From Legal and human rights issues of AI: Gaps, challenges and vulnerabilities (2020) · cites this paper as Siemaszko, Rodrigues, Slokenberga, 2020
The available passages identify the report and its AI-and-robotics focus (title page, p. 2; contributor information), but none addresses legal personality for AI systems or repeated EU cautions against creating it. Searches for legal personality, electronic personhood, and related terms returned only front matter. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
Legal and human rights issues of AI: Gaps, challenges and vulnerabilities
Rodrigues, R. (2020) · Journal of Responsible Technology · doi:10.1016/j.jrt.2020.100005 · Cited by 494 · see citing works
Self-citation
- Authors
- Siemaszko, K., Rodrigues, R., & Slokenberga, S.
- Affiliations
- Helsinki Foundation for Human Rights
- Trilateral Research & Consulting (GB)
- Uppsala University (SE)
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2020-07-30
- OpenAlex
- W3209891891
- Cited by
- 3
- References
- 0
- Citations by year
- 2023: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Funders
- European Commission (741716)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroethics, Human Enhancement, Biomedical Innovations
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Physiology › Biomedical Ethics and Regulation
- Keywords
- human enhancement, AI regulation, robotics regulation, legal framework, human rights, ethics and law, enforcement of legislation, policy recommendations, European Union law
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
