Property and the Construction of the Information Economy: A Neo-Polanyian Ontology

This chapter considers the changing roles and forms of information property within the political economy of informational capitalism. I begin with an overview of the principal methods used in law and in media and communications studies, respectively, to study information property, considering both what each disciplinary cluster traditionally has emphasized and newer, hybrid directions. Next, I develop a three-part framework for analyzing information property as a set of emergent institutional formations that both work to produce and are themselves produced by other evolving political-economic arrangements. The framework considers patterns of change in existing legal institutions for intellectual property, the ongoing dematerialization and datafication of both traditional and new inputs to economic production, and the emerging logics of economic organization within which information resources (and property rights) are mobilized. Finally, I consider the implications of that framing for two very different contemporary information property projects, one relating to data flows within platform-based business models and the other to information commons

A Delimitation of Data Sovereignty from Digital and Technological Sovereignty

Digital technology significantly impacts our everyday social lives and how we conduct business. This development results in an abundance of new data generated by people and organizations. Subsequently, future technological instruments must ensure data sovereignty that empowers individuals to maintain control over their data. However, data sovereignty is still blurry and conceptually overlaps with similar terminologies, such as digital and technological sovereignty. From an Information Systems (IS) point of view, delimiting data sovereignty from digital and technological sovereignty is crucial, creating a uniform understanding, especially for data ecosystems. Our study contributes to sharpening data sovereignty with a systematic literature review of 81 articles. It concludes that data sovereignty mainly drives IS activities by protecting data assets on individual and organizational levels. In contrast, digital sovereignty is shaped by digital expertise and interoperability, while technological sovereignty is the broadest concept with regulations and relations on an international level

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

A Forensic Enabled Data Provenance Model for Public Cloud

Cloud computing is a newly emerging technology where storage, computation and services are extensively shared among a large number of users through virtualization and distributed computing. This technology makes the process of detecting the physical location or ownership of a particular piece of data even more complicated. As a result, improvements in data provenance techniques became necessary. Provenance refers to the record describing the origin and other historical information about a piece of data. An advanced data provenance system will give forensic investigators a transparent idea about the data\u27s lineage, and help to resolve disputes over controversial pieces of data by providing digital evidence. In this paper, the challenges of cloud architecture are identified, how this affects the existing forensic analysis and provenance techniques is discussed, and a model for efficient provenance collection and forensic analysis is proposed

Taxonomy of Technological IT Outsourcing Risks: Support for Risk Identification and Quantification

The past decade has seen an increasing interest in IT outsourcing as it promises companies many economic benefits. In recent years, IT paradigms, such as Software-as-a-Service or Cloud Computing using third-party services, are increasingly adopted. Current studies show that IT security and data privacy are the dominant factors affecting the perceived risk of IT outsourcing. Therefore, we explicitly focus on determining the technological risks related to IT security and quality of service characteristics associated with IT outsourcing. We conducted an extensive literature review, and thoroughly document the process in order to reach high validity and reliability. 149 papers have been evaluated based on a review of the whole content and out of the finally relevant 68 papers, we extracted 757 risk items. Using a successive refinement approach, which involved reduction of similar items and iterative re-grouping, we establish a taxonomy with nine risk categories for the final 70 technological risk items. Moreover, we describe how the taxonomy can be used to support the first two phases of the IT risk management process: risk identification and quantification. Therefore, for each item, we give parameters relevant for using them in an existing mathematical risk quantification model