Artificially Intelligent Copyright: Rethinking Copyright Boundaries

My dissertation explores the legal boundaries of copyright law in the wake of artificial intelligence (AI) technology. In building the theoretical foundations for my dissertation, I go through several key phases. First, I highlight important historical events and milestones in AI. I further develop the philosophical debate on AI legal personhood and deliberate whether we are approaching a singularity the next stage of AI evolution. I also explore the concept of AI as it matured through the years. In the second part, I theorize how AI can be regarded as an author under IP normative standards. Part of accepting the argument that AI deserve copyright is a willingness to change the perception that only human creations are worthy of copyright protection. I also seek an answer to two sub-questions the who and the what. The who considers the normative standards of authorship in the ongoing struggle between an authors right and the public domain. The what raise the originality debate and discusses the standard of creation. In the third part, I outline the many candidates for AI authorship the programmer, the user, the AI and an alternative legal framework for AIs ownership like the public domain or author-in-law. Finally, I discuss the outcomes of each model and provide my conclusions

Linked democracy : foundations, tools, and applications

Chapter 1Introduction to Linked DataAbstractThis chapter presents Linked Data, a new form of distributed data on theweb which is especially suitable to be manipulated by machines and to shareknowledge. By adopting the linked data publication paradigm, anybody can publishdata on the web, relate it to data resources published by others and run artificialintelligence algorithms in a smooth manner. Open linked data resources maydemocratize the future access to knowledge by the mass of internet users, eitherdirectly or mediated through algorithms. Governments have enthusiasticallyadopted these ideas, which is in harmony with the broader open data movement

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Language representations for computational argumentation

Argumentation is an essential feature and, arguably, one of the most exciting phenomena of natural language use. Accordingly, it has fascinated scholars and researchers in various fields, such as linguistics and philosophy, for long. Its computational analysis, falling under the notion of computational argumentation, is useful in a variety of domains of text for a range of applications. For instance, it can help to understand users’ stances in online discussion forums towards certain controversies, to provide targeted feedback to users for argumentative writing support, and to automatically summarize scientific publications. As in all natural language processing pipelines, the text we would like to analyze has to be introduced to computational argumentation models in the form of numeric features. Choosing such suitable semantic representations is considered a core challenge in natural language processing. In this context, research employing static and contextualized pretrained text embedding models has recently shown to reach state-of-the-art performances for a range of natural language processing tasks. However, previous work has noted the specific difficulty of computational argumentation scenarios with language representations as one of the main bottlenecks and called for targeted research on the intersection of the two fields. Still, the efforts focusing on the interplay between computational argumentation and representation learning have been few and far apart. This is despite (a) the fast-growing body of work in both computational argumentation and representation learning in general and (b) the fact that some of the open challenges are well known in the natural language processing community. In this thesis, we address this research gap and acknowledge the specific importance of research on the intersection of representation learning and computational argumentation. To this end, we (1) identify a series of challenges driven by inherent characteristics of argumentation in natural language and (2) present new analyses, corpora, and methods to address and mitigate each of the identified issues. Concretely, we focus on five main challenges pertaining to the current state-of-the-art in computational argumentation: (C1) External knowledge: static and contextualized language representations encode distributional knowledge only. We propose two approaches to complement this knowledge with knowledge from external resources. First, we inject lexico-semantic knowledge through an additional prediction objective in the pretraining stage. In a second study, we demonstrate how to inject conceptual knowledge post hoc employing the adapter framework. We show the effectiveness of these approaches on general natural language understanding and argumentative reasoning tasks. (C2) Domain knowledge: pretrained language representations are typically trained on big and general-domain corpora. We study the trade-off between employing such large and general-domain corpora versus smaller and domain-specific corpora for training static word embeddings which we evaluate in the analysis of scientific arguments. (C3) Complementarity of knowledge across tasks: many computational argumentation tasks are interrelated but are typically studied in isolation. In two case studies, we show the effectiveness of sharing knowledge across tasks. First, based on a corpus of scientific texts, which we extend with a new annotation layer reflecting fine-grained argumentative structures, we show that coupling the argumentative analysis with other rhetorical analysis tasks leads to performance improvements for the higher-level tasks. In the second case study, we focus on assessing the argumentative quality of texts. To this end, we present a new multi-domain corpus annotated with ratings reflecting different dimensions of argument quality. We then demonstrate the effectiveness of sharing knowledge across the different quality dimensions in multi-task learning setups. (C4) Multilinguality: argumentation arguably exists in all cultures and languages around the globe. To foster inclusive computational argumentation technologies, we dissect the current state-of-the-art in zero-shot cross-lingual transfer. We show big drops in performance when it comes to resource-lean and typologically distant target languages. Based on this finding, we analyze the reasons for these losses and propose to move to inexpensive few-shot target-language transfer, leading to consistent performance improvements in higher-level semantic tasks, e.g., argumentative reasoning. (C5) Ethical considerations: envisioned computational argumentation applications, e.g., systems for self-determined opinion formation, are highly sensitive. We first discuss which ethical aspects should be considered when representing natural language for computational argumentation tasks. Focusing on the issue of unfair stereotypical bias, we then conduct a multi-dimensional analysis of the amount of bias in monolingual and cross-lingual embedding spaces. In the next step, we devise a general framework for implicit and explicit bias evaluation and debiasing. Employing intrinsic bias measures and benchmarks reflecting the semantic quality of the embeddings, we demonstrate the effectiveness of new debiasing methods, which we propose. Finally, we complement this analysis by testing the original as well as the debiased language representations for stereotypically unfair bias in argumentative inferences. We hope that our contributions in language representations for computational argumentation fuel more research on the intersection of the two fields and contribute to fair, efficient, and effective natural language processing technologies

Towards the generalisation of a case-based aiding system to facilitate the understanding of ethical and professional issues in computing

Modern computers endow users of Information and Computer Technology (ICT) with immense power. The speed of the computing revolution has enabled the novel implementation of ICT before consideration of consequent ethical issues can be made. There is now a demand by society that students, ICT novices, and professionals should be aware of the social, legal, and professional issues associated with ubiquitous use of computers. This thesis describes the development of an Internet-based tool that may be used to raise students' awareness of the ethical implications of ICT. It investigates the application, meaning, and scope of computer ethics. Theoretical foundations are developed for the construction of the tool that will classify, store, and retrieve a suitable analogous case from a collection of realworld, ethically analysed ICT case studies. These are used for comparison with ethically dubious events that may be experienced by students. The model draws upon the theoretical aspects of mechanisms for the modification of users' ethical perception. This research is novel in linking these theories to ethical understanding and case retrieval. Little information is available upon the retrieval of documents addressing ethical issues. The classification and retrieval of material using an ethical framework has some commonality with legal retrieval. Similarities are investigated, and concepts are adapted for the retrieval of ethical documents. The differences that arise present challenges for new research. The use of artificial intelligence (AI) retrieval techniques is not acceptable to meet the pedagogic aims of the retrieval tool. A model is developed, avoiding the use of AI in the reasoning process, requiring the student to consider and evaluate the ethical issues raised. The model is tested and evaluated. The research suggests that non-AI paradigms may be used for retrieval of ethical cases, and that areas for future investigation and development exist

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

The Proceedings of the 23rd Annual International Conference on Digital Government Research (DGO2022) Intelligent Technologies, Governments and Citizens June 15-17, 2022

The 23rd Annual International Conference on Digital Government Research theme is “Intelligent Technologies, Governments and Citizens”. Data and computational algorithms make systems smarter, but should result in smarter government and citizens. Intelligence and smartness affect all kinds of public values - such as fairness, inclusion, equity, transparency, privacy, security, trust, etc., and is not well-understood. These technologies provide immense opportunities and should be used in the light of public values. Society and technology co-evolve and we are looking for new ways to balance between them. Specifically, the conference aims to advance research and practice in this field. The keynotes, presentations, posters and workshops show that the conference theme is very well-chosen and more actual than ever. The challenges posed by new technology have underscored the need to grasp the potential. Digital government brings into focus the realization of public values to improve our society at all levels of government. The conference again shows the importance of the digital government society, which brings together scholars in this field. Dg.o 2022 is fully online and enables to connect to scholars and practitioners around the globe and facilitate global conversations and exchanges via the use of digital technologies. This conference is primarily a live conference for full engagement, keynotes, presentations of research papers, workshops, panels and posters and provides engaging exchange throughout the entire duration of the conference

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Foundations of B2B electronic contracting

Nowadays, flexible electronic cooperation paradigms are required for core business processes to meet the speed and flexibility requirements dictated by fast-changing markets. These paradigms should include the functionality to establish the formal business relationship required by the importance of these core processes. The business relationship should be established in an automated, electronic way in order to match the speed and flexibility requirements mentioned above. As such, it should considerably improve on the ineffectiveness and inefficiency of traditional contracting in this context. The result of the establishment should be a detailed electronic contract that contains a complete specification of the intended cooperation between organizations. Electronic contracts should contain a precise and unambiguous specification of the collaboration at both the conceptual and technological level. Existing commercial software solutions for business-to-business contracting provide low level of automation and concentrate solely on the automated management of the contract enactment. However, in the modern, dynamic, business settings, an econtracting system has to support high automation of the e-contract establishment, enactment, and management. In the thesis, the business, legal, and technological requirements for the development of a highly automated e-contracting system are investigated. Models that satisfy these requirements and that can be used as a foundation for the implementation of an electronic contracting system are defined. First, the thesis presents the business benefits introduced to companies by highly automated electronic contracting. Next, a data and process analysis of electronic contracting is presented. The specification of electronic contracts and the required process support for electronic contract establishment and enactment are investigated. The business benefits and data and process models defined in the thesis are validated on the basis of two business cases from on-line advertising, namely the cases of online advertising in "De Telegraaf" and "Google". Finally, the thesis presents a specification of the functionalities that must be provided by an e-contracting system. A conceptual reference architecture that can be used as a starting point in the design and implementation of an electronic contracting system is defined. The work in the thesis is conducted on the intersection of the scientific areas of conceptual information and process modeling and specification on the one hand and distributed information system architecture modeling on the other hand