Literatura académica sobre el tema "Criminal liability of artificial intelligence"
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Artículos de revistas sobre el tema "Criminal liability of artificial intelligence"
Seongjo Ahn. "Artificial Intelligence and Criminal Liability". Korean Journal of Legal Philosophy 20, n.º 2 (agosto de 2017): 77–122. http://dx.doi.org/10.22286/kjlp.2017.20.2.003.
Texto completoKirpichnikov, Danila, Albert Pavlyuk, Yulia Grebneva y Hilary Okagbue. "Criminal Liability of the Artificial Intelligence". E3S Web of Conferences 159 (2020): 04025. http://dx.doi.org/10.1051/e3sconf/202015904025.
Texto completoРадутний, Олександр Едуардович. "Criminal liability of the artificial intelligence". Problems of Legality, n.º 138 (27 de septiembre de 2017): 132–41. http://dx.doi.org/10.21564/2414-990x.138.105661.
Texto completo황만성. "A Study about Criminal Liability of Artificial Intelligence". 법과정책 24, n.º 1 (marzo de 2018): 361–84. http://dx.doi.org/10.36727/jjlpr.24.1.201803.012.
Texto completoKhisamova, Zarina y Ildar Begishev. "Criminal Liability and Artificial Intelligence: Theoretical and Applied Aspects". Russian Journal of Criminology 13, n.º 4 (23 de agosto de 2019): 564–74. http://dx.doi.org/10.17150/2500-4255.2019.13(4).564-574.
Texto completoRahman, Rofi Aulia y Rizki Habibulah. "THE CRIMINAL LIABILITY OF ARTIFICIAL INTELLIGENCE: IS IT PLAUSIBLE TO HITHERTO INDONESIAN CRIMINAL SYSTEM?" Legality : Jurnal Ilmiah Hukum 27, n.º 2 (6 de noviembre de 2019): 147. http://dx.doi.org/10.22219/jihl.v27i2.10153.
Texto completoShestak, Victor y Aleksander Volevodz. "Modern Requirements of the Legal Support of Artificial Intelligence: a View from Russia". Russian Journal of Criminology 13, n.º 2 (26 de abril de 2019): 197–206. http://dx.doi.org/10.17150/2500-4255.2019.13(2).197-206.
Texto completoLouis, Mark, Angelina Anne Fernandez, Nazura Abdul Manap, Shamini Kandasamy y Sin Yee Lee. "ARTIFICIAL INTELLIGENCE: IS IT A THREAT OR AN OPPORTUNITY BASED ON ITS LEGAL PERSONALITY AND CRIMINAL LIABILITY?" Journal of Information System and Technology Management 6, n.º 20 (1 de marzo de 2021): 01–09. http://dx.doi.org/10.35631//jistm.620001.
Texto completoKamalova, G. G. "SOME QUESTIONS OF CRIMINAL LEGAL RESPONSIBILITY IN THE FIELD OF APPLICATION OF ARTIFICIAL INTELLIGENCE SYSTEMS AND ROBOTICS". Bulletin of Udmurt University. Series Economics and Law 30, n.º 3 (26 de junio de 2020): 382–88. http://dx.doi.org/10.35634/2412-9593-2020-30-3-382-388.
Texto completoChernyh, Evgeniya. "Artificial intelligence in the Russian healthcare sector: current situation and criminal and legal risks". Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2020, n.º 4 (11 de diciembre de 2020): 127–31. http://dx.doi.org/10.35750/2071-8284-2020-4-127-131.
Texto completoTesis sobre el tema "Criminal liability of artificial intelligence"
Wang, Gang, Hsinchun Chen y Homa Atabakhsh. "Automaticially Detecting Deceptive Criminal Identities". ACM, 2004. http://hdl.handle.net/10150/106000.
Texto completoFear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.
Xu, Jennifer J. y Hsinchun Chen. "Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks". Elsevier, 2004. http://hdl.handle.net/10150/106207.
Texto completoEffective and efficient link analysis techniques are needed to help law enforcement and intelligence agencies fight organized crimes such as narcotics violation, terrorism, and kidnapping. In this paper, we propose a link analysis technique that uses shortest-path algorithms, priority-first-search (PFS) and two-tree PFS, to identify the strongest association paths between entities in a criminal network. To evaluate effectiveness, we compared the PFS algorithms with crime investigatorsâ typical association-search approach, as represented by a modified breadth-first-search (BFS). Our domain expert considered the association paths identified by PFS algorithms to be useful about 70% of the time, whereas the modified BFS algorithmâ s precision rates were only 30% for a kidnapping network and 16.7% for a narcotics network. Efficiency of the two-tree PFS was better for a small, dense kidnapping network, and the PFS was better for the large, sparse narcotics network.
Claussén, Karlsson Matilda. "Artificial Intelligence and the External Element of the Crime : An Analysis of the Liability Problem". Thesis, Örebro universitet, Institutionen för juridik, psykologi och socialt arbete, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-58269.
Texto completoSharma, Agni. "Assigning Liability in an Autonomous World". Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1531.
Texto completoMerege, Fernando. "Identificação de padrões de criminosos seriais usando inteligência artificial associada a neurônios espelhos". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-21052015-164058/.
Texto completoThe serial criminals who operate in the commission of the crime of theft have different modes of operation (modus operandi) and which may be identified through the analysis of forensic examinations using neural networks. In the proposed system, identified a particular mode of operation, a forensic analyst using the information collected and the hypotheses generated by field experts have the competence to define sets of complementary expert shares, which will be added to the records so identified. During a new forensic examination, in real time, the auxiliary subroutine examines data blocks sent by forensic experts in the field and, in the case of similarity to previously identified a mode of operation, sends them a complementary set of actions that the discretion of the responsible in the field, or can not be used to change the procedure chosen field. In this paper we define Mirror Neurons as the association of neural networks to identify patterns with the worksheet, used by forensic analyst for the definition of complementary actions, with the auxiliary subroutine that checks the blocks of information received and that can identify parts of a mode of operation, referring to field experts a set of complementary actions. This definition should be discovered by the neurobiology of a specific type of neuron that has the ability to shoot while receiving a sensory \"input\" activating an area of memory that, in consequence, can activate other areas of memory or send a motor command. This work programs of neural network used for identifying the modes of operation and the final part were developed, in addition, the worksheets for the elaboration of complementary actions and the auxiliary subroutine for real-time identification of the modes of partial operation. Network training was performed with 98 occurrences and validity check 10 events were used.
Haviland, Hannah. ""The Machine Made Me Do It!" : An Exploration of Ascribing Agency and Responsibility to Decision Support Systems". Thesis, Linköping University, Centre for Applied Ethics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2922.
Texto completoAre agency and responsibility solely ascribable to humans? The advent of artificial intelligence (AI), including the development of so-called “affective computing,” appears to be chipping away at the traditional building blocks of moral agency and responsibility. Spurred by the realization that fully autonomous, self-aware, even rational and emotionally-intelligent computer systems may emerge in the future, professionals in engineering and computer science have historically been the most vocal to warn of the ways in which such systems may alter our understanding of computer ethics. Despite the increasing attention of many philosophers and ethicists to the development of AI, there continues to exist a fair amount of conceptual muddiness on the conditions for assigning agency and responsibility to such systems, from both an ethical and a legal perspective. Moral and legal philosophies may overlap to a high degree, but are neither interchangeable nor identical. This paper attempts to clarify the actual and hypothetical ethical and legal situations governing a very particular type of advanced, or “intelligent,” computer system: medical decision support systems (MDSS) that feature AI in their system design. While it is well-recognized that MDSS can be categorized by type and function, further categorization of their mediating effects on users and patients is needed in order to even begin ascribing some level of moral or legal responsibility. I conclude that various doctrines of Anglo legal systems appear to allow for the possibility of assigning specific types of agency – and thus specific types of legal responsibility – to some types of MDSS. Strong arguments for assigning moral agency and responsibility are still lacking, however.
Racek, Libor. "Trestní odpovědnost umělé inteligence". Master's thesis, 2020. http://www.nusl.cz/ntk/nusl-411530.
Texto completoChao, Shih-wei y 趙士瑋. "Study of Artificial Intelligence Product Tort Liability: Focusing on Autonomous Vehicles". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fug776.
Texto completo國立交通大學
科技法律研究所
107
When autonomous vehicles (AVs) eventually begin to fill our roads, we are sure to be sorely reminded, aside from their numerous benefits, of the risk they bring forth in the form of accidents. This thesis attempts to explore the identification, management and allocation of AV-related risks from a tort liability perspective. It is first discovered that current tort liability schemes fail to address AVs properly, not only due to the “responsibility gap” caused by the autonomy and unpredictability of modern artificial intelligence systems, but also since both driver liability and product liability regulations exhibit fatal flaws in accounting for the attributes of such a revolutionary technology. This thesis instead proposes a liability scheme catered towards AVs. First and foremost, AVs are divided into two categories, fully- and non-fully-autonomous, according to whether the role of “driver” is present within. Non-fully-autonomous AVs should follow the traditional automobile accident liability paradigm where the driver is primarily responsible for the injury. Fully-autonomous AVs, on the other hand, give rise to a more sophisticated resolution of accidents. Initially, the victim should be entitled to partial but immediate compensation from an AV-injury public fund. Then, in court, the victim should be allowed to make claims against the AV owner, lending from vicarious liability theory, and against the manufacturer in terms of strict liability. This thesis aspires to involve all stakeholders regarding AV safety and liability, with aim to safeguard unfortunate AV accident victims, and ultimately build confidence among the public towards a future including AVs.
CHANG, CHAN y 張湛. "The Civil Liability of Artificial Intelligence System Users—Focusing on Autonomous Vehicles". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9cwbqk.
Texto completo國立中正大學
財經法律系研究所
107
Autonomous vehicles, one of the most crucial parts of artificial intelligence, will soon be mass-produced and commercialized. Especially when the concepts of drivers and driving no longer exist in level 4 and 5 autonomous vehicles, it raises an important issue about the imputation in a car accident. In U.S. Law, the liability in the car accident is transformed from autonomous vehicles users to manufacturers. Meanwhile, the concept of manufacturer enterprise responsibility is also proposed as a solution, in which the manufacturers share the liability of personal damage caused by the autonomous vehicles. In our country, we can use product liability enacted under article 7 of Consumer Protection Act to tackle the problem of liability in an accident. Furthermore, service liability in Consumer Protection Act, which the manufacturers should compensate consumers or the third parties when the transportation service they provide fails to meet the contemporary technical and professional standards with reasonably expected safety, can be used to deal with the problem as well. The author tries to examine Contract Law in our country with autonomous vehicle. First, autonomous vehicles will concern issues related to defect warranty and the liability of non-performance after they are commercialized. Among others, the establishment of accessory obligation and collateral obligation is prominent. Besides, the transportation service with autonomous vehicles are related to the contract of hire of work and carriage of passengers. The author tries to blaze a trail by observing the autonomous vehicles as quasi-entities to deal with the humanlike characteristic of artificial intelligence, and hope to construct a more rigorous legal system.
Urban, Martin. "Umělá inteligence a odpovědnost za její jednání". Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-388942.
Texto completoLibros sobre el tema "Criminal liability of artificial intelligence"
Hallevy, Gabriel. Liability for Crimes Involving Artificial Intelligence Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10124-8.
Texto completoQuattrocolo, Serena. Artificial Intelligence, Computational Modelling and Criminal Proceedings. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52470-8.
Texto completoservice), SpringerLink (Online, ed. Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York, NY: Springer New York, 2012.
Buscar texto completoCastella, Eduardo Marcelo. Investigação criminal e informática: Inteligência artificial x boletim de ocorrência (BO), soluções em KMAI. Curitiba: Juruá Editora, 2005.
Buscar texto completoProcedure, Judicial Conference of the United States Committee on Rules of Practice and. Preliminary draft of proposed amendments to the Federal rules of criminal procedure. Wilmette, Ill. (3201 Old Glenview Rd., Wilmette 60091): Callaghan, 1986.
Buscar texto completoMontano, Pedro J. La responsabilidad penal de médicos y científicos ante las nuevas tecnologías de la procreación: Con especial referencia a las recomendaciones europeas y al Pacto de San José de Costa Rica. Montevideo: A.M. Fernández, 1991.
Buscar texto completoNissan, Ephraim. Computer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation. Dordrecht: Springer Netherlands, 2012.
Buscar texto completoPagallo, Ugo. The Laws of Robots: Crimes, Contracts, and Torts. Dordrecht: Springer Netherlands, 2013.
Buscar texto completoSubrahmanian, V. S. Handbook of Computational Approaches to Counterterrorism. New York, NY: Springer New York, 2013.
Buscar texto completoCapítulos de libros sobre el tema "Criminal liability of artificial intelligence"
Hallevy, Gabriel. "Basic Requirements of Modern Criminal Liability". En Liability for Crimes Involving Artificial Intelligence Systems, 29–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_2.
Texto completoMuftic’, Nasir. "Liability for artificial intelligence". En Digital Technologies and the Law of Obligations, 95–118. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003080596-7.
Texto completoKingston, J. K. C. "Artificial Intelligence and Legal Liability". En Research and Development in Intelligent Systems XXXIII, 269–79. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47175-4_20.
Texto completoHallevy, Gabriel. "Punishibility of Artificial Intelligence Technology". En Liability for Crimes Involving Artificial Intelligence Systems, 185–227. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_6.
Texto completoHallevy, Gabriel. "External Element Involving Artificial Intelligence Systems". En Liability for Crimes Involving Artificial Intelligence Systems, 47–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_3.
Texto completoBaker, Dennis J. y Paul H. Robinson. "Emerging technologies and the criminal law". En Artificial Intelligence and the Law, 1–30. Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780429344015-1.
Texto completoSchild, Uri J. "Criminal Sentencing and Intelligent Decision Support". En Judicial Applications of Artificial Intelligence, 47–98. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9010-5_3.
Texto completoHallevy, Gabriel. "Artificial Intelligence Technology and Modern Technological Delinquency". En Liability for Crimes Involving Artificial Intelligence Systems, 1–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_1.
Texto completoHallevy, Gabriel. "Positive Fault Element Involving Artificial Intelligence Systems". En Liability for Crimes Involving Artificial Intelligence Systems, 67–146. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_4.
Texto completoHallevy, Gabriel. "Negative Fault Elements and Artificial Intelligence Systems". En Liability for Crimes Involving Artificial Intelligence Systems, 147–84. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10124-8_5.
Texto completoActas de conferencias sobre el tema "Criminal liability of artificial intelligence"
Borges, Georg. "AI systems and product liability". En ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3462757.3466099.
Texto completoMaurushat, Alana, Lyria Bennett-Moses y David Vaile. "Using 'big' metadata for criminal intelligence". En ICAIL '15: 15th International Conference on Artificial Intelligence and Law. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2746090.2746110.
Texto completoThelisson, Eva. "Towards Trust, Transparency and Liability in AI / AS systems". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/767.
Texto completoWang, Xing, Yixin Sun, Xiaoliang Tang, Ji Chen y Jiuxiang Jin. "Interchange of criminal rules between CLRL and LKIF". En ICMAI '18: 2018 International Conference on Mathematics and Artificial Intelligence. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3208788.3208802.
Texto completoSushina, Tatyana y Andrew Sobenin. "Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities". En 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200526.062.
Texto completoYuan, Danding. "Case Study of Criminal Law Based on Multi-task Learning". En 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 2020. http://dx.doi.org/10.1109/icaice51518.2020.00025.
Texto completoLi, Shang, Boyang Liu, Lin Ye, Hongli Zhang y Binxing Fang. "Element-Aware Legal Judgment Prediction for Criminal Cases with Confusing Charges". En 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00097.
Texto completoKrungklang, Weerayut y Sukree Sinthupinyo. "An Analysis of Natural Language Text Relating to Thai Criminal Law". En 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2020. http://dx.doi.org/10.1109/ecai50035.2020.9223143.
Texto completoHan, Jinbo, Dakui Li, Nanhai Yang, Zhu Liu y Qiong Nan. "Analysis of Criminal Case Judgment Documents Based on Deep Learning". En 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/acaai-18.2018.61.
Texto completoKaya, Mustafa, Betul Ay Karakus y Serkan Karakus. "Binary Classification of Criminal Tools from the Images of the Case Using CNN". En 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620886.
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