AI as a legal person

Abstract: The idea of the legal personhood of artificial intelligence (AI) — the idea that intelligent agents can have rights and incur obligations under the law— is controversial, and in fact is often dismissed out of hand: in this paper I will argue that, on the contrary, such legal personhood may be the next big challenge for our legal systems, and we need it to deal with the new kinds ofcomplexity introduced by AI. Furthermore, I argue that we already have experiences we can look: to this end we can draw on the reasoning applied to the legal personhood recognized for corporations and other nonhuman entities. In order to do this, I address some of the criticisms against ascribing legal personhood to AI. I also look at the Canadian and EU ethical guidelines so as to keep the development of AI within the framework of human values, and I show that an ascription of legal personhood to AI is consistent with them. I also address a few of the big issues involved in making the legal personhood of AI a reality.This paper is part of the project supported by the CONEX programme and has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N. 600371, el Ministerio de Economia, Industria y Competitividad (COFUND2014-51509), el Ministerio de Educacion, Cultura y Deporte (CEI-15-17) and Banco SantanderUniversidad Carlos III de Madrid (APC. Read & Publish Agreement CRUE-CSIC 2023

SOAP: Efficient Feature Selection of Numeric Attributes

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going to use, that normally will be compensated, in greater or smaller extent, by the attribute reduction in the classification model. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(mn log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; with no need for transformation. The performance of SOAP is analysed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [6] and ReliefF [11]. The results are generated by C4.5 and 1NN before and after the application of the algorithms

Criminal liability of autonomous agents: from the unthinkable to the plausible

Series : Lecture notes in computer science, ISSN 0302-9743, vol. 8929The evolution of information technologies have brought us to a point where we are confronted with the existence of agents - computational entities - which are able to act autonomously with little or no human intervention. And their behavior can damage individual or collective interests that are protected by criminal law. Based on the analysis of different models of criminal responsibility of legal persons - which constituted an interesting advance in the criminal law in rela-tion to what was hitherto traditionally accepted -, we will appraise whether the necessary legal elements to have direct criminal liability of artificial entities are present.This work is part-funded by CROWDSOURCING project (Reference: DER2012-39492-C02-01)

An architecture for establishing legal semantic workflows in the context of integrated law enforcement

A previous version of this paper was presented at the Third Workshop on Legal Knowledge and the Semantic Web (LK&SW-2016), EKAW-2016, November 19th, Bologna, ItalyTraditionally the integration of data from multiple sources is done on an ad-hoc basis for each to "silos" that prevent sharing data across different agencies or tasks, and is unable to cope with the modern environment, where workflows, tasks, and priorities frequently change. Operating within the Data to Decision Cooperative Research Centre (D2D CRC), the authors are currently involved in the Integrated Law Enforcement Project, which has the goal of developing a federated data platform that will enable the execution of integrated analytics on data accessed from different external and internal sources, thereby providing effective support to an investigator or analyst working to evaluate evidence and manage lines of inquiries in the investigation. Technical solutions should also operate ethically, in compliance with the law, and subject to good governance principles

Legal linked data ecosystems and the rule of law

This chapter introduces the notions of meta-rule of law and socio-legal ecosystems to both foster and regulate linked democracy. It explores the way of stimulating innovative regulations and building a regulatory quadrant for the rule of law. The chapter summarises briefly (i) the notions of responsive, better and smart regulation; (ii) requirements for legal interchange languages (legal interoperability); (iii) and cognitive ecology approaches. It shows how the protections of the substantive rule of law can be embedded into the semantic languages of the web of data and reflects on the conditions that make possible their enactment and implementation as a socio-legal ecosystem. The chapter suggests in the end a reusable multi-levelled meta-model and four notions of legal validity: positive, composite, formal, and ecological