Digitally Assisted Collective Governance of Smart City Commons - ARTIO

Lead Research Organisation: University of Leeds
Department Name: Sch of Computing

Abstract

The aim of this fellowship programme is to design a socially responsible collective governance for Smart City commons: shared pool of urban resources (transport, parking space, energy) managed and regulated digitally. Smart City commons exhibit unprecedented complexity and uncertainties: transport systems integrate electric, shared and autonomous vehicles, while distributed energy resources highly penetrate energy systems. How can we manage Smart City commons in a sustainable and socially responsible way to tackle long-standing problems such as traffic jams, overcrowded parking spaces or blackouts? Failing to digitally coordinate collective decisions promptly and at large-scale has tremendous economic, social and environmental impact. Coordinated decisions require a digital (r)evolution, a new paradigm on where we decide, how we decide and what we decide.

But which are limiting factors? 1.Online decision-making often disconnects citizens from the physical urban space for which decisions are made: choices are less informed and vulnerable to social media misinformation, while decision outcomes may show lower legitimation. What if collective choices could be made more locally as digital geolocated testimonies, creating opportunities for community interactions and deliberation? 2.Voting system design is another origin of poor collective decisions, with majority voting often failing to achieve consensus or fair and legitimate outcomes. What if we expanded the design space of voting systems with alternative voting methods, e.g. preferential, to encompass social values? While such methods have so far been costly and limited to low-cognitive exercises, negating their social value over majority voting, decision-support systems based on artificial intelligence (AI) emerge as game-changer. 3.With an immense computational and communication complexity, large-scale coordination of inter-dependent collective decisions remains a timely grand challenge. What if coordination could be digitally assisted and emerge as a result of smart aggregate information exchange, achieving privacy and efficiency?

To address these challenges, I will combine Internet of Things, human-centred AI and blockchain technology with social choice theory and mechanism design. Using IoT devices, urban points of interest can be turned into digital voting centres within which conditions for a more informed decision-making will be verified in the blockchain, e.g. proving citizens' location. A novel ontology of voting features will provide the basis to predict voting methods that generate fair and legitimate outcomes. Using collective and active reinforcement learning techniques on the blockchain, human and machine collective intelligence will be combined to achieve a trustworthy coordination of collective decisions at large scale.

In collaboration with high-profile partners from government/industry, I will demonstrate the applicability of these approaches via 4 innovative impact cases. 1.Using the developed solutions, citizens will geolocate problems and vote for transport planning solutions. 2.They will also vote on spot to implement participatory budgeting projects. 3.A smart parking system will be enhanced with load-balancing capabilities to alleviate crowded and polluted city centres. 4.Via citizens' coordination of transport modality, an urban traffic control system will be optimized for an equitable shift to public/sharing transport, while preserving low-carbon transport zones.

These Smart City blueprints will open up new avenues for deeper understanding of digitally assisted collective governance. To master this inter-disciplinary research area and develop myself into a future leader, I will visit world-class leaders and, together with my team, enrol in novel training activities. Two esteemed mentors and an advisory board will further support me. I will engage with the broader community of citizens and policy-makers by organizing workshops and hackathons.
 
Description The most significant achievements from the award so far have been the building of a novel collective privacy recovery method using decentralized artificial intelligence (AI). Here we introduce for first time a scalable and practical data-sharing coordination capability to collectively recover significant privacy for people, while letting data collectors provide online services of high quality with lower cost and risks. Such win-win coordination would not be possible to achieve at scale by humans alone due to the (combinatorial) complexity of the computational problem behind collective privacy recovery. Via data-sharing collective arrangements assisted by decentralized trustworthy AI, we finally make feasible to share data under the doctrine "as little as possible, as much as necessary", which has so far failed to find practicality and applicability. Our study provides a comprehensive understanding of the criteria that influence data-sharing decisions under different conditions: from privacy attitudes, to intrinsic, rewarded and coordinated data sharing. This is a striking advancement in the field that moves far beyond existing privacy protection techniques or mainstream privacy-intrusive artificial intelligence. With this key finding, we aspire to set a stepping stone for a long-awaited renaissance of privacy in the digital era.

These key finding directly contribute to the overarching aim of this fellowship: to build socially responsible and sustainable governance systems of Smart City commons based on trustworthy digitally assisted collective decisions. Here, our key findings touch upon the huge amount of sensitive personal data generated by Smart City socio-technical infrastructures and how communities (data collectives) can manage their sharing with lower risks. This finding also contributes to the following objectives of this fellowship: O3: Build mechanisms of human-machine collective intelligence to coordinate citizens' decisions and solve complex governance problems of Smart City commons. O4: Empower trust on the studied decision-support systems without relying on single third parties and by aligning with citizens' collective interests.

Moreover, the fellowship has generated key findings on the legitimacy of voting methods. In particular, we demonstrate evidence that the legitimacy of collective decision outcomes can be improved via multi-option preferential voting rules instead of majority voting. These findings contribute to the following objective: Determine the design of voting systems for outcomes that encompass social values such as fairness, legitimacy, consensus and participation in governing Smart City commons.
Exploitation Route The findings might be taken forward by industry (service providers, data centers) as a way to lower costs and risks of data storage. It may also support policy-makers and communities to align data sharing with the privacy values of citizens. These findings will be taken forward by policy-makers and designers of voting campaigns at a next stage of the project.
Sectors Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Environment,Government, Democracy and Justice,Security and Diplomacy,Other

URL https://doi.org/10.48550/arXiv.2301.05995
 
Description This fellowship project has created the first key results with a direct impact on democracy and citizens' participation. In particular, findings on the legitimacy of voting methods (https://dx.doi.org/10.2139/ssrn.4372245) are used to inform the design of a participatory budgeting campaign in the city of Aarau in Switzerland. The campaign brings several innovations, for instance, testing a combination of cumulative voting and the method of equal shares with the aim for a more fair election of funded projects, higher inclusion of minorities, higher legitimacy and participation. Moreover, this effort also involves the development of new open source software features and the collection of new open data to be published.
First Year Of Impact 2023
Sector Communities and Social Services/Policy,Government, Democracy and Justice
Impact Types Cultural,Societal,Policy & public services

 
Description Adoption of Fair Participatory Budgeting (Method of Equal Shares)
Geographic Reach Local/Municipal/Regional 
Policy Influence Type Contribution to new or improved professional practice
Impact This method of equal shares is a completely different approach to elect winners in participatory budgeting campaigns. It promotes proportionality and prevents marginalization of communities, fairer allocation of budgets and inclusion of minorities. This method may create new opportunities for trust to direct democracy, improved economic impacts, changed of public attitudes towards direct participation and improved efficiency in delivery of public services.
URL https://www.stadtidee.aarau.ch/abstimmungsphase.html/1937
 
Description New Edge-Cloud Infrastructure for Distributed Intelligent Computing
Amount £50,000 (GBP)
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 07/2022 
End 03/2023
 
Title Improvements on the collective learning method of EPOS 
Description This is a multi-agent distributed combinatorial optimization method that is supporting our research and the study of new applications. 
Type Of Material Improvements to research infrastructure 
Year Produced 2018 
Provided To Others? Yes  
Impact Expanded functionality: hard constraints satisfaction allowing us to tackle new optimization problems and applications. 
URL http://epos-net.org
 
Title Stanford Participatory Budgeting 
Description A voting platform for participatory budgeting campaigns 
Type Of Material Improvements to research infrastructure 
Year Produced 2023 
Provided To Others? Yes  
Impact In collaboration with the development team in Stanford, we introduced new features such as cumulative voting, the support of the German language and the CHF currency. With these improvements, the platform will be used for voting in the city of Aarau in Switzerland. 
URL https://github.com/StanfordCDT/pb
 
Title Agent-based Planning Portfolio 
Description This dataset contains a portfolio of different datasets with generated plans for agents that represent multiple options to choose from. The following datasets are included so far: 1. Synthetic 2. Residential energy consumption 3. Bike sharing 4. Charging control of electric vehicles 5. Fog computing load-balancing 6. Home appliances peak-shaving 7. Data collectives These portfolio of agent plans has been used to study the collective learning algorithm of EPOS, the Economic Planning and Optimized Selections: http://epos-net.org 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact Two new datasets were uploaded: - Data collectives - UAV swarm intelligence for distributed sensing These datasets were collected to allow the research outlined in the following papers respectively: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. Qin, C. and Pournaras, E., 2022. Coordination of Drones at Scale: Decentralized Energy-aware Swarm Intelligence for Spatio-temporal Sensing. arXiv preprint arXiv:2212.14116. 
URL https://figshare.com/articles/dataset/Agent-based_Planning_Portfolio/7806548/5
 
Title Data Collectives 
Description Human experimentation data on data sharing decisio conducted at the DeSciL lab at ETH Zurich 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact This dataset allowed us to conduct an ambitious research on privacy and build an AI solution to achieve collective privacy recovery. This work is featured in the following paper: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. 
URL https://figshare.com/articles/dataset/Data_Collectives/21750158/1
 
Description Distributed optimization and UAV swarm intelligence 
Organisation Lappeenranta University of Technology
Country Finland 
Sector Academic/University 
PI Contribution We provided expertise on collective learning and distributed optimization for two problems studied in LUT: - UAV last mile delivery - Job shop scheduling
Collaborator Contribution New application domains to test and study our optimization methods
Impact Two papers under consideration for publication. Two more papers are ongoing work.
Start Year 2022
 
Description How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms 
Organisation Karlsruhe Institute of Technology
Country Germany 
Sector Academic/University 
PI Contribution I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science.
Collaborator Contribution Invitation to workshop and invitation to contribute to a forthcoming book.
Impact Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book.
Start Year 2022
 
Description How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms 
Organisation University of Bonn
Country Germany 
Sector Academic/University 
PI Contribution I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science.
Collaborator Contribution Invitation to workshop and invitation to contribute to a forthcoming book.
Impact Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book.
Start Year 2022
 
Description How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms 
Organisation University of Vienna
Country Austria 
Sector Academic/University 
PI Contribution I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science.
Collaborator Contribution Invitation to workshop and invitation to contribute to a forthcoming book.
Impact Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book.
Start Year 2022
 
Description Implementation of new voting methods for participatory budgeting 
Organisation Stanford University
Country United States 
Sector Academic/University 
PI Contribution - Using the Stanford Participatory Budgeting platform (https://pbstanford.org) for first time in Europe - Proposing and designing new features for the platform - Knowledge exchange
Collaborator Contribution Implementation of new features on the Stanford Participatory Budgeting platform (https://pbstanford.org)
Impact - New supported features on the Stanford Participatory Budgeting platform (https://pbstanford.org). - New designed voting campaign to be tested with the platform - Further development and maintenance of open-source software
Start Year 2022
 
Description Policy-making for Sustainable Mobility 
Organisation Oxfordshire County Council
Country United Kingdom 
Sector Public 
PI Contribution The contributions are outlined as follows: - Knowledge exchange about our experience with the city of Aarau and the participatory budgeting campaign. - Contribution to an organized workshop in Oxfordshire - Ongoing preparation of a study on smart mobility
Collaborator Contribution The contributions are outlined as follows: - Organization of a workshop - Sharing insights and data about smart mobility in Oxfordshire
Impact An outline of the outputs includes the following: - An organized workshop - A first concept of a study on smart mobility to be performed in Oxfordshire
Start Year 2022
 
Description Trust and legitimation in the digital democracy 
Organisation ETH Zurich
Country Switzerland 
Sector Academic/University 
PI Contribution - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods
Collaborator Contribution The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation
Impact The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science.
Start Year 2020
 
Description Trust and legitimation in the digital democracy 
Organisation Swiss National Science Foundation
Country Switzerland 
Sector Public 
PI Contribution - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods
Collaborator Contribution The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation
Impact The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science.
Start Year 2020
 
Description Trust and legitimation in the digital democracy 
Organisation University of Fribourg
Country Switzerland 
Sector Academic/University 
PI Contribution - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods
Collaborator Contribution The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation
Impact The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science.
Start Year 2020
 
Description White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems 
Organisation University of Sheffield
Department Automatic Control and Systems Engineering
Country United Kingdom 
Sector Academic/University 
PI Contribution This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield.
Collaborator Contribution The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits.
Impact An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems).
Start Year 2021
 
Description White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems 
Organisation University of York
Department Department of Computer Science
Country United Kingdom 
Sector Academic/University 
PI Contribution This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield.
Collaborator Contribution The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits.
Impact An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems).
Start Year 2021
 
Description White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems 
Organisation White Rose University Consortium
Country United Kingdom 
Sector Academic/University 
PI Contribution This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield.
Collaborator Contribution The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits.
Impact An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems).
Start Year 2021
 
Title Data Collectives 
Description AI system, documentation and analysis of coordinated data sharing of data collectives 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
Impact This software made possible to complete the following work: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. 
URL https://zenodo.org/record/7457574
 
Description ASDA Lida Health & Sustainability Hackathon 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Industry/Business
Results and Impact This activity was a hackathon organized by ASDA and the Leeds Institute of Data Analytics (LIDA). The goal of this hackathon was to design and build solutions for healthy and sustainable consumption for the customers of ASDA. I participated in both the ideation phase as well as in the main hackathon event. My fellowship team also participated in the final hackathon event. Around 40-50 participants were split in 4 groups working on different aspects of sustainable consumption, for instance, carbon footprint, healthy recipes, etc. Together with my team, we provided expertise and shared lessons learnt from our award-winning Horizon EU project ASSET. The event was invaluable to build links to industry, know the datasets available and build a collaboration on decision-support systems for empowering sustainable consumption.
Year(s) Of Engagement Activity 2023
 
Description Competition law and beyond: efficient digital markets through privacy, taxation, and consumer protection 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This was a highly inter-disciplinary event to which I contributed as an invited speaker and panel expert on the topic of "Interdisciplinary Approaches to Digital Markets". The participants of the events were mainly lawyers, social scientists, economists and policy makers from university, public and non-governmental organizations. Participants were both online and physically present. The panel discussions provided new insights to professionals and practitioners about the design of digital markets and how they can be regulated for the protection of consumers' privacy and rights.
Year(s) Of Engagement Activity 2022
URL https://www.leeds.ac.uk/main-index/events/event/212/jean-monnet-centre-of-excellence-on-digital-gove...
 
Description Invited Talk at the webinar of the Global Food and Environment Institute: A novel App to support the discovery and choices of more sustainable products 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This was an invited talk to the September 2022 webinar of the Global Food and Environment Institute. I talked about my work on "How value-sensitive design can empower sustainable consumption", which received the GFEI Food and Agriculture Awards 2021. The event had 20-30 participants and other 70 views online in YouTube. My talk triggered interest and I had a follow up invitation to participate in the ASDA LIDA Healthy and Sustainable Consumption Hackathon event.
Year(s) Of Engagement Activity 2022
URL https://www.youtube.com/watch?v=cwf_vtYLeyc
 
Description Invited talk at the Hackathon "Participatory Resilience" at ETH Zurich 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact The goal of this talk was to give a hands on tutorial on collective learning and the EPOS optimization systems:

Pournaras, E., Pilgerstorfer, P. and Asikis, T., 2018. Decentralized collective learning for self-managed sharing economies. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 13(2), pp.1-33. https://doi.org/10.1145/3277668

The students and practitioners participated in the hackathon used the EPOS toolkit to run Smart City optimization scenarios that improve the resilience of socio-technical infrastructures in a more participatory and decentralized way.

The talk was received with enthusiasm and strengthened the ongoing collaboration with ETH Zurich. A follow up invited talk is made for the event "Co-Creating the Future: Participatory Cities and Digital Governance": https://www.participatorycities.net
Year(s) Of Engagement Activity 2022
URL http://participatoryresilience.ch
 
Description Invited talk to the workshop: "Beyond Quantity: Research with Subsymbolic AI" 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This workshop consisted of participants and speakers from many different disciplines interested in AI and how AI is changing science. This included anthropologists, media artists, philosophers, sociology and computer science. Based on the outcomes of this workshop, the organizer is preparing a book with the contributions of the workshop. My talk covers the the topic of "Managing Smart City Commons with Human-machine Collective Intelligence" and I am planning a contribution to the forthcoming book. Moreover, the organizer is planning a visit at University of Leeds in Sept. 2023 to continue and strengthen the collaboration.
Year(s) Of Engagement Activity 2022
URL https://howisaichangingscience.eu/conference-beyond-quantity/