Artificial Intelligence-Enabled Data Value Curation on AI-Data Commons

Sustainability, regulation and environmental issues such as climate change and resource scarcity are emerging as key trends with decisive impact on company’s Risk management, value creation and growth strategy. This combination represents one of the biggest opportunities to Society as a whole, including organizations, Governments and citizens. Typically, companies possess vast amounts of data, most of it unutilized. Many are now making investments in digital transformation, which generates even more data. The issue is how to generate social impact returns. The use of data and data analytics is centuries old, but with Artificial Intelligence (AI), Machine Learning (ML), jointly with other distributed ledger technologies (Blockchain, Cloud) that are advancing rapidly, there are major opportunities to capture value better, cheaper and faster. Speed is of the essence, and success depends on how fast organizations understand the need for non-financial risks management and respond to data-driven intelligence by reallocating resources to accomplish what needs to be done more efficiently. The reason for impact returns is understanding the benefit as a common value, not exclusive to companies, but it also has to distribute value among individuals, communities, and why not, to contribute to regenerate our planet based on a new economy.

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A Topography of Data Commons: From Regulation to Private Dynamism

GRUR International ◽

10.1093/grurint/ikab156 ◽

2021 ◽

Author(s):

Andrea Ottolia ◽

Cristiana Sappa

Keyword(s):

Artificial Intelligence ◽

Intellectual Property Rights ◽

Digital Technology ◽

The Internet ◽

Legal Rules ◽

Additional Information ◽

Processing Techniques ◽

The Internet Of Things ◽

Data Commons ◽

Ipr Protection

Abstract Knowledge is subject to enclosure through digital technology and legal rules. Data collected, stored and pooled by the Internet of Things (IoT) or Artificial Intelligence (AI) are no exception to this. Operators acting in the markets related to the algorithmic society may have a quite diversified range of intellectual property rights (IPRs) to protect the information they produce and manage. This is exploited through algorithmic processing techniques, aggregating collected data for the generation of new ones, thus creating additional information and knowledge. This paper studies whether and when data, information and knowledge, presented within the Big Data, IoT and AI structures, may be considered and exploited as commons. The analysis is not aimed at stating that commons should be the general solution for the algorithmic society. Nor does it endorse legal interpretations unilaterally favoring openness and limiting IPR protection and privacy rules (though this could be the case under certain circumstances). The question is to establish whether a certain level of commons should be provided by regulation or left to spontaneous private initiatives. In this regard, two different meanings of data commons are used in this work. The first one refers to the open access systems provided by regulation, equivalent to a public domain protection, and opposed to exclusivity mechanisms. The second refers to data commons which are privately ‘constructed’ on top of background regulation and manage resources for a limited set of claimants.

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Data Economy 2.0: From Big Data Value to AI Value and a European Data Space

The Elements of Big Data Value ◽

10.1007/978-3-030-68176-0_16 ◽

2021 ◽

pp. 379-399

Author(s):

Sonja Zillner ◽

Jon Ander Gomez ◽

Ana García Robles ◽

Thomas Hahn ◽

Laure Le Bars ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Big Data ◽

Point Of View ◽

Driven Systems ◽

Significant Growth ◽

Data Space ◽

European Data ◽

Data Value ◽

Data Volume

AbstractArtificial intelligence (AI) has a tremendous potential to benefit European citizens, economy, environment and society and already demonstrated its potential to generate value in various applications and domains. From a data economy point of view, AI means algorithm-based and data-driven systems that enable machines with digital capabilities such as perception, reasoning, learning and even autonomous decision making to support people in real scenarios. Data ecosystems are an important driver for AI opportunities as they benefit from the significant growth of data volume and the rates at which it is generated. This chapter explores the opportunities and challenges of big data and AI in exploiting data ecosystems and creating AI value. The chapter describes the European AI framework as a foundation for deploying AI successfully and the critical need for a common European data space to power this vision.

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