Criteria for Recognition of AI as a Legal Person
Bibliographic record
Abstract
This question of AI legal personhood is mostly theoretical today. In article we try to generalize some common ways that existing in legal theory and practice. We analyze some cases of recognition of untypical legal persons as well enacted statements in Europe and USA. Readers will not find a detailed methodology in the paper, but rather a list of criteria that is helpful to make a decision on granting legal personhood. Practices of European Union and the United States indicate that common approaches to the legal personality of some kinds of AI are already developed. Both countries are strongly against legal personhood of intellectual war machines. Liability for any damage of misbehavior of military AI is still on competence of military officers. In case of civil application of AI there are two options. AI could be as legal person or as an agent of business relations with other legal persons. Every legal person has to be recognized as such by society. All untypical legal persons have wide recognition of society. When considering the issue of introducing a new legal person into the legal system, legislators must take into account the rights of already existing subjects. Policy makers have to analyze how such legal innovation will comply with previous legal order, first of all how it will affect the fundamental rights and freedoms of the human beings. The legal personhood of androgenic robots that can imitate human behavior regarded in paper as a good solution to minimize illegal and immoral acts committed with their involvement. It would be a factor that keep people from taking action against robots very similar to people. Authors conclude that key factors would be how society will react to a new legal person, how changing of legal rules will affect legal system and why it is necessary. At least all new untypical legal persons are recognized by society, affects of the legal system in manageable way and brings definite benefits to state and society.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".