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Record W7014841319

Robots Are People Too…Maybe

2019· article· en· W7014841319 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueeYLS (Yale Law School) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigitalization, Law, and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsRobotEmpathySocial robotRoboticsHuman rightsHuman–robot interactionPersonhood
DOInot available

Abstract

fetched live from OpenAlex

Movies like Transformers and television shows like Westworld invite viewers to see robots as human. Silicon Valley has yet to produce such lifelike entities, but lawmakers are already considering how to assign rights and responsibilities to robots and their creators. In 2017, the European Parliament proposed an “electronic persons” status for robots, quickly provoking criticism from robotics researchers and legal experts as “inappropriate.” Instead of creating a new legal status, Professor Ignacio Cofone of McGill University Faculty of Law recommends in a recent paper that lawmakers classify robots and other artificial intelligence entities on a “continuum between tools and people.” Cofone argues that by determining whether a particular robot most resembles a tool, corporation, animal, child, or adult, regulators can assign legal rights and responsibilities to the robot, its creator, or its user. He contends that legal treatment should depend on three core characteristics: the robot’s ability to interact with the world, the foreseeability of its actions, and the way people perceive it. The most important of these three characteristics, Cofone writes, is how humans perceive the robot. Cofone terms this “social valence.” The more empathy people feel toward a robot—the more human they think it is—the more vulnerable people will be in relation to it. A robot with high social valence could be capable of deceiving people, Cofone warns. It “could pretend to care for our interest” but in fact be programmed to serve “the commercial interests of other people.” He emphasizes that humans have extensive experience in defending themselves against deception by other people, but so far they have virtually no experience defending against deception by robots. Regulators designing consumer protections should consider a robot’s social valence in their analysis, Cofone urges. Cofone also argues that the foreseeability of a robot’s actions should determine “how liable other people should be” for a robot’s actions. He contends that if a robot could make its own decisions, that would justify allocating liability to the robot itself, rather than its creator. Such a system would require the creation of legal incentives to which robots could—and would—respond. But experts agree that robots do not yet possess the ability to make their own decisions. Cofone acknowledges that today’s primary regulatory issues concern when to hold creators responsible for their robots. That analysis, he states, should depend on the foreseeability of a robot’s actions. Cofone uses the example of Tay—a short-lived Microsoft chatbot—to illustrate how foreseeability can be used to evaluate product liability for robots. He writes that within 16 hours of online human interaction, Tay unexpectedly “became racist and sexist, denied the Holocaust, and supported Hitler.” Although the United States protects free speech, in other countries, such as Germany, Tay’s statements would have been criminal. Liability should depend on the degree to which Microsoft could have foreseen Tay’s behavior, Cofone argues. He acknowledges that regulators could impose strict liability on robot creators to encourage maximum caution, but argues that a foreseeability analysis is preferable because in tort law “one is rarely responsible for what one cannot foresee.” Cofone also explores a third characteristic of robots—their physical form, or “embodiment.” But he emphasizes that plenty of artificial intelligence technology can affect the world without a physical presence: smart home thermostats, trading algorithms, and Siri, for example. For that reason, he concludes that embodiment is not essential when assigning rights and responsibilities to robots. Cofone concludes his paper by addressing robot rights. Beyond allocating responsibility for harms caused by robots, he argues that regulators should consider how to allocate rights as well. These concerns include whether robots should have free speech and whether they should own the copyright for the work they produce—and if they should not own it, who should? Questions of rights should be addressed in conjunction with questions of responsibility, Cofone suggests. By evaluating robots according to their social valence and emergence, Cofone argues, regulators can place individual robots on a “continuum between tools and people” to determine their rights and responsibilities.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.259
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it