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Robots Are People Too…Maybe

2019· article· en· W7014841319 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDigitalization, Law, and Regulation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRobotEmpathySocial robotRoboticsHuman rightsHuman–robot interactionPersonhood
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,154

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,017
Communication savante0,0070,015
Science ouverte0,0010,005
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0460,022

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,259
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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