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Digital Twins: On Algorithm-Based Political Participation
Abstract
The aim of this chapter is to critically analyse the challenges facing the application of participatory processes based on Artificial intelligence algorithms in the political sphere, as well the possibilities these can offer. Particular attention is paid to the proposal for an augmented democracy based on digital twin technology and design principles, which is being created and developed within a number of technological and academic fields. Currently, civic disaffection towards participation in a variety of political processes, such as law-making and the creation of strategies and public policies, as well as political representatives’ oft-encountered inability to take up the voices of the citizens has become an increasing hindrance to the development of modern societies. In response to these social and political challenges, the academic and scientific-technological world has offered new proposals for political participation, such as César A. Hidalgo’s augmented democracy, which proposes adapting and applying technology developed for industrial manufacture: the digital twin. Unlike the algorithmic governance proposals of Matsuda, Gerritsen, Zaripov, Asker Bryl Staunæs or Romanian government, Hidalgo proposes the need to work from the perspective of automated direct democracy, which is based on digital twins —algorithmic and individualized clones of citizens— and this technology is considered capable of both articulating the voice of the people and acting in accordance with it.
... More recently, this need for intermediation was questioned first, by the proponents of e-democracy and web 2.0. solutions [3][4][5][6], and more recently, by work exploring the use of artificial intelligence (AI) to augment democratic participation [7][8][9][10][11]. ...
... In brief, augmented democracy is the idea of using software agents to explore fine-grained forms of civic participation. These are forms that interpolate between representative and direct forms of democracy, where individuals not only choose among representatives, but can directly indicate their preferences on policy proposals [7][8][9][10]. ...
... The idea of augmented democracy also builds on recent work showing that LLMs can be used to simulate human participants in surveys [19][20][21][22], which has shown, for instance, that LLMs provide similar moral judgements to humans [20] and can be used to construct fine-grained personas and predict their electoral behaviour [19]. On the philosophical side, the exploration of LLMs has focused mostly on the critical and ethical comparison of different forms of digital democracy, including augmented democracy [9,10,33,34,34,35]. ...
We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens’ preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil’s 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject’s individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a ‘bundle rule’, which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation.
This article is part of the theme issue ‘Co-creating the future: participatory cities and digital governance’.
Prevailing top-down systems in politics and economics struggle to keep pace with the pressing challenges of the 21st century, such as climate change, social inequality and conflict. Bottom-up democratisation and participatory approaches in politics and economics are increasingly seen as promising alternatives to confront and overcome these issues, often with utopian overtones, as proponents believe they may dramatically reshape political, social and ecological futures for the better and in contrast to contemporary authoritarian tendencies across various countries. Institutional specifics and the associated collective human behavior or culture remains little understood and debated, however. In this article, I propose a novel research agenda focusing on utopian democratisation efforts with formal and computational methods as well as with artificial intelligence - I call this agenda Artificial Utopia. Artificial Utopias provide safe testing grounds for new political ideas and economic policies in-silico with reduced risk of negative consequences as compared to testing ideas in real-world contexts. An increasing number of advanced simulation and intelligence methods, that aim at representing human cognition and collective decision-making in more realistic ways, could benefit this process. This includes agent-based modelling, reinforcement learning, large language models and more. I clarify what some of these simulation approaches can contribute to the study of Artificial Utopias with the help of two institutional examples: the citizen assembly and the democratic firm.
Drawing on a two-way democracy, the aim of this book is intended as an aid for thinking through viable alternatives to the current state of democracy with regard to its ethical foundations and the moral knowledge implicit in or assumed by the way we perceive and understand democracy. It is intended to stimulate reflection and discussion on the basis that, by addressing what we understand as democracy, we can inevitably influence the reality known as democracy. Democracy’s evident regression in today’s world makes this all too apparent: it has become a hostage to all kinds of autocracies and technopopulisms, which are supported to a greater or lesser extent by the current algorithmic revolution.
Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programmes by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.
The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability.
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short- Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour.
Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering , the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission who wishes to delegate a significant role to Digital Twins in addressing climate change and supporting Green Deal policy. As Digital Twins give rise to high expectations, ambitions, and are being entrusted important societal roles, it is crucial to critically reflect on the nature of Digital Twins. In this article, we therefore philosophically reflect on Digital Twins by critically analysing dominant conceptualisations, the assumptions underlying them, and their normative implications. We dissect the concept and argue that a Digital Twin does not merely fulfil the role of being a representation, but is in fact a steering technique used to direct a physical entity towards certain goals by means of multiple representations. Currently, this steering seems mainly fuelled by a reductionist approach focused on efficiency and optimisation. However, this is not the only direction from which a Digital Twin can be thought and, consequently, designed and deployed. We therefore set an agenda based on a critical understanding of Digital Twins that helps to draw out their beneficial potential, while addressing their potential issues.
A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology’s history and main concepts. A number of application examples of digital twins for personalised medicine, public health and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.
Keywords: digital twins; human digital twins; precision medicine; personalised medicine; precision public health
URL: https://www.mdpi.com/2075-4426/11/8/745/htm
Das Konzept der Gesundheitskompetenz hat in den vergangenen Jahren viel Beachtung erfahren. Wesentliche Ansätze sind das Bereitstellen von Gesundheitsinformationen, das Vermitteln durch Bildungsprogramme und der Transfer über Kommunikationstechniken. Aus zahnmedizinischer Sicht fällt dem individuellen Gespräch zwischen Patient und Zahnarzt eine besondere Bedeutung zu („sprechende Zahnmedizin“).
Ziel unseres Beitrags ist es, den bisherigen Kenntnisstand über die Zusammenhänge zwischen Mundgesundheitskompetenz und sprechender Zahnmedizin darzustellen, bereits vorhandene Konzepte zur Vermittlung kommunikativer Kompetenzen vorzustellen und weiteren Handlungsbedarf aus der Sicht der Bundeszahnärztekammer aufzuzeigen. Mehrere gesundheitspsychologische Modelle betonen einen positiven Zusammenhang zwischen Mundgesundheitskompetenz, Mundgesundheitsverhalten und Mundgesundheit. Weitere Bestimmungsfaktoren scheinen die Selbstwirksamkeitsüberzeugung des Patienten und die individuelle Interaktion mit dem Zahnarzt zu sein. Deshalb scheint es zielführend, im Sinne der sprechenden Zahnmedizin anlassbezogen zu intervenieren. Das persönliche Gespräch zwischen Zahnarzt und Patient kann dabei Beratung, Aufklärung, Motivation und praktische Anleitung abdecken und zu einer partizipativen Entscheidungsfindung führen.
Angebote für das Erlernen und Vertiefen von Kommunikationstechniken stellen wichtige Hilfestellungen für die Praxis dar. Einige stellt dieser Beitrag beispielhaft vor. Um die sprechende Zahnmedizin auf breiter Ebene zu etablieren, bedarf es allerdings weitergehender Angebote und Anreize. Mit unseren Vorschlägen möchten wir Wege aufzeigen, wie die sprechende Zahnmedizin als integraler Bestandteil der zahnmedizinischen Versorgung weiter gefördert und gefestigt werden kann.
The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion (Dietvorst et al. J Exp Psychol 144(1):114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts of democracy and the Rule of Law. To provoke further consideration and illustrate that initial intuitions regarding democratic innovation may not always be accurate, I pose and explore four ways that AI and other forms of technology could be used to augment the representative democratic process. The augmentations range from voting online to the wholesale replacement of the legislature’s human representatives with algorithms. After first noting the intuition that less invasive forms of augmented democracy may be less objectionable than more extreme forms, I go on to critically assess whether the augmentation of existing systems satisfies or enhances ideas associated with democracy and the Rule of Law (provided by Dahl and Fuller). By imagining a (not too far-fetched) future in a (not too far-removed) democratic society, my conclusion is that, when it comes to democracy and the Rule of Law, intuitions regarding technology may lead us astray.
(Español) La gestión de datos masivos se ha convertido en uno de los grandes retos de la sociedad digitalmente hiperconectada. Si bien se reconoce que la recopilación, procesamiento y uso de datos masivos ge- nera importantes avances en esferas como la economía, la sanidad, la po- lítica, la comunicación, la educación o la ciencia, al mismo tiempo existen fuertes críticas por los efectos disruptivos y las consecuencias negativas que este proceso puede generar sobre la sociedad, especialmente entre los grupos más vulnerables de esta. El objetivo de este trabajo es pro- poner orientaciones para el diseño de un sistema de gobernanza ética (ethical data governance) que, basado en la participación, el diálogo y las buenas prácticas y apoyado en diversas técnicas y tecnologías digitales, permita una recopilación, procesamiento y uso socialmente responsable y moralmente válido de datos masivos.
(English) Big data management has become one of the great challeng- es for the digitally hyperconnected society. Although it is acknowledged that the compilation, processing and use of Big Data generates impor- tant advances in areas like the economy, health, politics, communication, education and science, at the same time there is strong criticism because of the disruptive effects and negative consequences this process can generate for society, especially among the most vulnerable groups. The aim of this study is to suggest guidelines for designing an ethical data governance system which, based on participation, dialogue and good practice and backed by various digital techniques and technologies, al- lows the socially responsible and morally valid compilation, processing and use of Big Data.
Transition towards Industry 4.0 relies heavily on manufacturing digitalisation. Digital twin plays a significant role among the pool of relevant technologies as a powerful tool that is expected provide digital access to detailed real-time monitoring of the physical processes and enable significant optimisation due to utilisation of big data acquired from them. Over the past years a significant number of works produced conceptual frameworks of digital twins and discussed their requirements and benefits. The research literature demonstrates application examples and proofs of concepts, although the content is less rich. This paper presents a generative model based on generative adversarial networks (GAN) for machining vibration data, discusses its performance and analyses the drawbacks. The proposed model includes process parameter inputs used to condition the features of generated signals. The control over the generator and a neural network architecture utilising techniques from style-transfer research provide the means to analyse the signal building blocks learned by the model and explore their relationship. The quality of the learned process representation is demonstrated using a dataset obtained from a machining time-domain simulation. The novel results constitute a critical component of a machining digital twin and open new research directions towards development of comprehensive manufacturing digital twins.
Actualmente la convergencia de la Inteligencia Artificial, el Big Data y el Internet de las cosas permite la algoritmización,
dataficación
e hiperconectividad de todo ámbito de actividad humana, como el económico, el asistencial o el político. Este hecho, junto con la desafección de la sociedad civil hacia los actuales modelos democráticos, está generando nuevas propuestas democráticas basadas en la supuesta objetividad, moralidad, representatividad y neutralidad de los modelos matemáticos basados en redes neuronales artificiales de aprendizaje automático. Utilizando una metodología hermenéutico-crítica en el estudio de fuentes bibliográficas, el objetivo del presente trabajo es mostrar los retos, límites y consecuencias que subyacen a las diversas propuestas democráticas basadas en modelos matemáticos de decisión, datos masivos y la hiperconectividad de la sociedad digital.
Currently the convergence of Artificial Intelligence, Big Data and the Internet of Things allows the algorithmization,
datafication
and hyperconnectivity of the entire scope of human activity, such as economics, welfare or politics. Together with the disaffection of civil society towards the current democratic models, this fact is generating new democratic proposals based on the supposed objectivity, morality, representability and neutrality of mathematical models based on artificial neural networks of machine learning. Using a hermeneutic-critical methodology in the study of bibliographical sources, the objective of the present work is to show the challenges, limits and consequences that underlie the diverse democratic proposals based on mathematical models of decision, massive data and the hyperconnectivity of the digital society.
State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives.
Political communication is the process of putting information, technology, and media in the service of power. Increasingly, political actors are automating such processes, through algorithms that obscure motives and authors yet reach immense networks of people through personal ties among friends and family. Not all political algorithms are used for manipulation and social control however. So what are the primary ways in which algorithmic political communication—organized by automated scripts on social media—may undermine elections in democracies? In the US context, what specific elements of communication policy or election law might regulate the behavior of such “bots,” or the political actors who employ them? First, we describe computational propaganda and define political bots as automated scripts designed to manipulate public opinion. Second, we illustrate how political bots have been used to manipulate public opinion and explain how algorithms are an important new domain of analysis for scholars of political communication. Finally, we demonstrate how political bots are likely to interfere with political communication in the United States by allowing surreptitious campaign coordination, illegally soliciting either contributions or votes, or violating rules on disclosure.
The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The paper aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The paper also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept.
Organizations’ communication and social responsibility have been conceived and used as two completely separate areas, whose relationship is merely instrumental. This has led to a current lack of trust in the mechanisms and procedures of corporate social responsibility information. This article proposes an ethics of communication that defines how to ethically manage corporate social responsibility communication which is capable of specifying the relationships that link ethics, communication, and responsibility. The objective is to present the basic traits of new codes of ethics generation that include both compliance measures and their external verification by ethical auditing. The proposal is important because it establishes an ethical infrastructure from which to recover trust in corporate social responsibility communication. The employed methodology is reconstruction, based on Habermas’ Theory of Communicative Action, and also transparency and participation claims that result from discourse ethics.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
This paper introduces the concept of a " Digital Twin " as a virtual representation of what has been produced. Compare a Digital Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.
Introduction:
The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.
Areas covered:
The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.
Expert opinion:
The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.
Deliberative and participatory approaches to democracy seek to directly include citizens in decision-making and agenda-setting processes. These methods date back to the very foundations of democracy in Athens, where regular citizens shared the burden of governance and deliberated every major issue. However, thinkers at the time rightly believed that these methods could not function beyond the scale of the city-state, or polis. Representative democracy as an innovation improved on the scalability of collective decision making, but in doing so, sacrificed the extent to which regular citizens could participate in deliberation. Modern technology, including advances in computational power, machine learning algorithms, and data visualization techniques, presents a unique opportunity to scale out deliberative processes. Here we describe Polis, an open source web application capable of collecting and synthesizing feedback from people in a scalable and distributed fashion. Polis has shown itself capable of building shared understanding, disincentivizing counterproductive behavior (trolling), and cultivating points of consensus. It has done this in the context of journalistic and academic research, and directly as part of decision-making bodies at local and national levels, directly affecting legislation. These results demonstrate that deliberative processes can be scaled up beyond the constraints of in-person gatherings and small groups.
This chapter reviews and evaluates different ways in which digital technologies may affect democracy. Specifically, the chapter develops a framework to evaluate democratic practices that is rooted in the tradition of deliberative democracy. The chapter then applies this framework to evaluate proposals of how technology may improve democracy. The chapter distinguishes three families of proposals, depending on the depth of the change that they affect. Mere changes, such as automatic fact-checking on social media, augment existing practices. Moderate reforms, such as apps that enable and reward participation in local government, facilitate new practices. Radical revisions, such as using artificial intelligence to replace parliaments, are constitutive of new practices often replacing existing ones. The chapter then concentrates on three radical revisions—Wiki democracy, avatar democracy, and data democracy—and identifies meaningful benefits in the first and deep problems in the latter two proposals.
This book challenges the idea that democratic processes are functionally short-sighted. Many observers assume that long-term issues will be ignored or discounted in democratic systems because of the myopic preferences of voters, the political dynamics of short electoral cycles, the exclusion (or absence) of future others in decision-making processes, and the reality that democratic processes are often captured by powerful actors with dominant short-term interests. The evidence is clear: we have poorly managed many long-term issues, including climate change, nuclear waste disposal, plastics pollution, natural disaster preparedness, infrastructure maintenance, and budget deficits. This idea—which Michael K. MacKenzie calls the “democratic myopia thesis”—is a sort of conventional wisdom: It is one of those things that scholars and pundits take for granted as a truth about democracy without subjecting it to adequate critical scrutiny. This book challenges this conventional wisdom and articulates a deliberative, democratic theory of future-regarding collective action. It is argued that each part of the democratic myopia problem can be addressed through democratic—rather than authoritarian—means. At a more fundamental level, the book argues that if democratic practices are world-making activities that empower us to make our shared worlds together, they should also be understood as future-making activities. Despite the short-term dynamics associated with electoral democracy, MacKenzie argues that inclusive and deliberative democratic processes are the only means we have for making our shared futures together in collectively intentional, mutually accommodating ways.
Der Band widmet sich dem Bereich Digitalisierung anhand unterschiedlicher Bezugspunkte zur Theorie und Praxis Politischer Bildung. Digitalisierung eröffnet den Bürger*innen einer demokratischen Gesellschaft neue Chancen für Partizipation und Informationsgewinn im eigenen Lebensbereich und bietet in diesem Kontext Potential für Demokratisierungsprozesse. Digitalisierung bedeutet gleichzeitig neue Herausforderungen für den demokratischen Alltag und die Politische Bildung. Die Beiträge fragen kritisch, wie sich Digitalisierung auf die Politische Bildung auswirkt und ob sie konstruktiv zugunsten der gesellschaftlichen Demokratisierung genutzt werden kann.
Die Herausgeber*innen
Lara Möller ist Universitätsassistentin für die Didaktik der Politischen Bildung am Zentrum für Lehrer*innenbildung der Universität Wien und Politikwissenschaftlerin am Demokratiezentrum Wien.
Prof. Dr. Dirk Lange lehrt Didaktik der Politischen Bildung an der Universität Wien und der Leibniz Universität Hannover. Er leitet das Demokratiezentrum Wien.
Smart Cities evolve into complex and pervasive urban environments with a citizens’ mandate to meet sustainable development goals. Repositioning democratic values of citizens’ choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm of augmented democracy that promises actively engaging citizens in a more informed decision-making augmented into public urban space. The proposed concept is inspired by a digital revive of the Ancient Agora of Athens, an arena of public discourse, a Polis where citizens assemble to actively deliberate and collectively decide about public matters. The core contribution of the proposed paradigm is the concept of proving witness presence: making decision-making subject of providing secure evidence and testifying for choices made in the physical space. This paper shows how the challenge of proving witness presence can be tackled with blockchain consensus to empower citizens’ trust and overcome security vulnerabilities of GPS localization. Moreover, a novel platform for collective decision-making and crowd-sensing in urban space is introduced: Smart Agora. It is shown how real-time collective measurements over citizens’ choices can be made in a fully decentralized and privacy-preserving way. Witness presence is tested by deploying a decentralized system for crowd-sensing the sustainable use of transport means. Furthermore, witness presence of cycling risk is validated using official accident data from public authorities compared against wisdom of the crowd. The paramount role of dynamic consensus, self-governance and ethically aligned artificial intelligence in the augmented democracy paradigm is outlined.
A generic framework for prognostics and health monitoring (PHM) which is rapidly deployable to heterogeneous fleets of assets would allow for the automation of predictive maintenance scheduling directly from operational data. Deep learning based PHM implementations provide part of the solution, but their main benefits are lost when predictions still rely on historical failure data and case-by-case feature engineering. We propose a solution to these challenges in the form of a Deep Digital Twin (DDT). The DDT is constructed from deep generative models which learn the distribution of healthy data directly from operational data at the beginning of an asset’s life-cycle. As the DDT learns the distribution of healthy data it does not rely on historical failure data in order to produce an estimation of asset health. This article presents an overview of the DDT framework and investigates its performance on a number of datasets. Based on these investigations, it is demonstrated that the DDT is able to detect incipient faults, track asset degradation and differentiate between failure modes in both stationary and non-stationary operating conditions when trained on only healthy operating data.
Dynamic manufacturing processes are characterized by a lack of coordination, complexity and sheer volumes of data. Digital transformation technologies offer the manufacturers the capability to better monitor and control both assets and production. This provides also an ever-improving ability to investigate new products and production concepts in the virtual world while optimizing future production with IoT-captured data from different devices and shop floor machine centres. In this study, a digital twin is presented for an assembly line, where IoT-captured data is fed back into the digital twin enabling manufacturers to interface, analyse and measure the performance in real-time of a manufacturing process. The digital twin concept is then applied to an assembly production plan found in the automotive industry, where actual data is considered to analyse how the digital duplicate can be used to review activities and improve productivity within all production shifts.
Future generations of NASA and U.S. Air Force vehicles will require lighter mass while being subjected to higher loads and more extreme service conditions over longer time periods than the present generation. Current approaches for certification, fleet management and sustainment are largely based on statistical distributions of material properties, heuristic design philosophies, physical testing and assumed similitude between testing and operational conditions and will likely be unable to address these extreme requirements. To address the shortcomings of conventional approaches, a fundamental paradigm shift is needed. This paradigm shift, the Digital Twin, integrates ultra-high fidelity simulation with the vehicle's on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.
Un físico del MIT propone cambiar a los políticos por un sistema de Inteligencia Artificial
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The Dawn of Digital Democracy
New Zealand Develops World’s First Artificial Intelligence Politician
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