Knowledge-Driven Data Ecosystems Toward Data Transparency

A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that DEs face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven DE architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Last, we discuss and rate the potential of the proposed architecture in the fulfillmentof these requirements.

Automated Annotations for AI Data and Model Transparency

The data and Artificial Intelligence revolution has had a massive impact on enterprises, governments, and society alike. It is fueled by two key factors. First, data have become increasingly abundant and are often available openly. Enterprises have more data than they can process. Governments are spearheading open data initiatives by setting up data portals such as data.gov and releasing large amounts of data to the public. Second, AI engineering development is becoming increasingly democratized. Open source frameworks have enabled even an individual developer to engineer sophisticated AI systems. But with such ease of use comes the potential for irresponsible use of data. Ensuring that AI systems adhere to a set of ethical principles is one of the major problems of our age. We believe that data and model transparency has a key role to play in mitigating the deleterious effects of AI systems. In this article, we describe a framework to synthesize ideas from various domains such as data transparency, data quality, data governance among others to tackle this problem. Specifically, we advocate an approach based on automated annotations (of both data and the AI model), which has a number of appealing properties. The annotations could be used by enterprises to get visibility of potential issues, prepare data transparency reports, create and ensure policy compliance, and evaluate the readiness of data for diverse downstream AI applications. We propose a model architecture and enumerate its key components that could achieve these requirements. Finally, we describe a number of interesting challenges and opportunities.

Urban Open Platform for Borderless Smart Cities

As a network of connected sensors to transform data into knowledge, Urban Platforms have been rooted in several smart city projects. However, this has often resulted in them being no more than IoT dashboards. More recently, there has been an increased interest in supporting the data governance and distributed architecture of Urban Platforms in order to adjust these with the administrative structure in a specific city. In addition, Urban Platforms also deal with data roaming between different stakeholders including other cities, different government levels, companies and citizens. Nevertheless, the first deployments have led to an inflexible “smart cities in a box” approach that does not help with building digital skills and causes vendor lock-in to products that do not scale. There is a need to start with simple and widespread urban services through a collaborative joint cross-border, hands-on effort. In order to meet the level of interoperability, international standards should be adopted. The aim of an Urban Open Platform (UOP), introduced in this paper, is to support not only data acquisition but also various types of data processing: data is aggregated, processed, manipulated and extended within the city context. Conceptually, special attention has been put on scalability, roaming and reliability in urban environments. This article introduces the UOP uniquely in the cross-border data exchange context of two European capital cities, Helsinki and Tallinn, and validates it with 10 real-life urban use cases.

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

Data Governance and Regulation for Sustainable Smart Cities

It is increasingly understood that data governance is a key variable in the endeavor to design smart cities in such a way that they effectively contribute to achieving sustainability goals and solving environmental problems. However, the question of how different governance options might affect sustainability goals is still open. This article suggests an approach to answering this question from a regulatory perspective. It draws some preliminary lessons from previous regulatory debates, proposes a prospective evaluation of ideal types of data regulation, and finally seeks to outline normative guidelines for social–ecological data governance.

COVID-19 reproduction number and non-pharmaceutical interventions in Lithuania

Currently the world is threatened by a global COVID-19 pandemic and it has induced crisis creating a lot of disruptions in the healthcare system, social life and economy. In this article we present the analysis of COVID-19 situation in Lithuania and it's municipalities taking into consideration the effect of non-pharmaceutical interventions on the reproduction number. We have analysed the period from 20/03/2020 to 20/06/2021 covering two quarantines applied in Lithuania. We calculated the reproduction number using the incidence data provided by State Data Governance Information System, while the information for applied non-pharmaceutical interventions was extracted from Oxford COVID-19 Government Response Tracker and the COVID-19 website of Government of the Republic of Lithuania. The positive effect of applied non-pharmaceutical interventions on reproduction number was observed when internal movement ban was applied in 16/12/2020 during the second quarantine in Lithuania.

Accountability through Sustainability Data Governance: Reconfiguring Reporting to Better Account for the Digital Acceleration

Accountability assessment is a highly relevant challenge for companies nowadays. The COVID-19 pandemic prompted a digital acceleration in business environments, which in turn brought more focus on sustainability practices that could help organizations better demonstrate their accountability, thus making them more resilient to the ever-changing socio-economic context. Therefore, this paper aims to evaluate how to further improve corporate accountability (on a strategic and operational level), taking advantage of the digitalization changes that companies are being forced to go through and applying them to the sustainability evaluation process, including the reporting as its final output. The first research outcome is a combined framework, based on data governance and sustainability literature models, seeking to optimize the manageability of sustainability data. The second outcome is a matrix, based on a content analysis of 20 sustainability reports, representing eight possible types of behavior that companies adopt when integrating digitalization practices into their sustainability evaluation process. The aim is to explore how the communication of digital activities could refine the diligence of the sustainability assessment process, with disclosure representing its last step. Finally, the ‘leading’ case was broken down into the general strategic components that could potentially be included in a balanced data-sustainability reporting strategy.