Large Techno Social Systems (LTSS) involve leveraging technological advancements and digital platforms to improve access to essential services, enhance quality of life, and ensure social inclusivity. In LTSS, people cannot be mere users of networked technologies and services designed for optimization purposes. Their behaviour should become one of the key levers for designing technologies turning them into real “Smart citizens” that teach their surrounding environment (and embedded devices) but learn reciprocally from it. LTSS can be realized by promoting smart communities which leverage technology, data, and innovation to improve the quality of life for its residents, enhance sustainability, and optimize the use of resources. Human-centric technology can empower citizens to actively engage in societal decision-making processes, participate in deliberative systems, and contribute to societal welfare. On the other hand, technological advancements, including data analytics and artificial intelligence, can inform evidence-based policymaking and planning processes. Indeed, digital technologies have the potential to influence human behaviour change by providing information, personalized feedback, social support, targeted interventions, and opportunities for learning. This work explores two approaches to realize LTSS driven smart communities that leverage digital technologies to achieve a higher collaboration and reciprocal learning between machines and people. On one hand, co-production in smart communities promotes behaviour change by empowering citizens in the co-design and co-delivery process, designing user-centric solutions, leveraging local knowledge, fostering collaboration, and facilitating capacity building. On the other hand, Citizen Science can inspire and enable behaviour change that leads to more sustainable, responsible, and community-oriented actions by promoting awareness, empowering individuals, and facilitating collaboration.

1. 1
Humanized Computing: the path towards
higher collaboration and reciprocal learning
between machines and people
Dalian University of Technology, Dalian, 29th February 2024
Prof. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es
@dipina

2. 2
Abstract
Abstract. Large Techno Social Systems (LTSS) involve leveraging technological advancements and digital platforms to improve
access to essential services, enhance quality of life, and ensure social inclusivity. In LTSS, people cannot be mere users of
networked technologies and services designed for optimization purposes. Their behaviour should become one of the key levers for
designing technologies turning them into real “Smart citizens” that teach their surrounding environment (and embedded devices)
but learn reciprocally from it. LTSS can be realized by promoting smart communities which leverage technology, data, and
innovation to improve the quality of life for its residents, enhance sustainability, and optimize the use of resources. Human-
centric technology can empower citizens to actively engage in societal decision-making processes, participate in deliberative
systems, and contribute to societal welfare. On the other hand, technological advancements, including data analytics and artificial
intelligence, can inform evidence-based policymaking and planning processes. Indeed, digital technologies have the potential to
influence human behaviour change by providing information, personalized feedback, social support, targeted interventions, and
opportunities for learning. This work explores two approaches to realize LTSS driven smart communities that leverage digital
technologies to achieve a higher collaboration and reciprocal learning between machines and people. On one hand, co-
production in smart communities promotes behaviour change by empowering citizens in the co-design and co-delivery process,
designing user-centric solutions, leveraging local knowledge, fostering collaboration, and facilitating capacity building. On the other
hand, Citizen Science can inspire and enable behaviour change that leads to more sustainable, responsible, and community-
oriented actions by promoting awareness, empowering individuals, and facilitating collaboration.

3. 3
LTSS: Large Techo-Social Systems
• Large Techo Social Systems (LTSS) involve leveraging
technological advancements and digital platforms to improve
social access to essential services, enhance quality of life, and
ensure social inclusivity.
– Ethical considerations, data privacy, digital divide, and inclusivity
must be addressed to ensure that the benefits of technology are
equitably distributed, and social welfare outcomes are achieved for
all members of society.
• Examples: Internet and social networks, electric power grids,
etc.

4. 4
Internet of People (IoP)
• People should not be mere users of
networked technologies and services
designed for optimization purposes (e.g.
automation for energy efficiency), but their
behaviour should become one of the key
levers for designing technologies turning
them into real “Smart citizens” that
participate in the digital sphere beyond
being testers, data providers or consumers.
Hybrid
Intelligence
Human
Behaviour-
change
Citizen
Science
Human in the Loop &
Semantic Interoperability
Sentient
Things
Internet
of
People
Situated IoT & Engaging
Interaction

5. 5
What is a Smart Community?
• A smart community refers to a community that leverages
technology, data, and innovation to improve the quality of life
for its members, enhance sustainability, and optimize the use
of resources.
– By utilizing smart technologies, a smart community aims to improve
efficiency, responsiveness, and the overall well-being of its
members
– LTSS form the backbone upon which smart communities are built

6. 6
Behaviour change
• Digital technologies have the potential to influence behavior
change by providing information, personalized feedback, social
support, targeted interventions, and opportunities for remote
learning.
– By leveraging the capabilities of digital technologies, individuals can
be empowered to adopt and sustain positive behaviors that align
with their goals and aspirations.
• Digital enablers: mobile apps, wearables, social networks, gamification
• Interventions: awareness, motivation, training, nudges, feedback

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Reciprocal Human-Machine Learning
• Systems where both humans and machines
learn and adapt based on their interactions
with each other
– It signifies a symbiotic relationship in which both
entities benefit from the insights and
knowledge of the other.
– It implies that disruptive technologies such as
Artificial Intelligence (AI) should work for people
and people should be able to trust AI
technologies

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Humanized Computing (HumanComp)
• Refers to the design and development of computing systems that are aware of human
needs, behaviours, and contexts
– Focuses on making technology more human-sensitive, accessible, and responsive to their emotions,
preferences, and social norms.
• Related to: user-centric design, accessibility, emotional intelligence in AI, and adaptive interfaces
• Aims to democratize the assembly of Smart Communities for the common good
– Enacts community-wide societal transformations based on behaviour change strategies and guided
behavioural interventions
– Applies reciprocal learning for the co-creation and co-valorisation of techno-social experiments in
different key socioeconomic areas such as health, environment
Sentient
Computing
(2002)
Social Objects
(2008)
Eco-aware
everyday
Things (2014)
Co-creation &
Human
Computation
(2015)
Sentient Things
(2017)
Internet of
People (2020)
Humanized
Computing
(2023)

9. 9
Humanized Computing (HumanComp)

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Co-production: co-design & co-delivery of
human-centric public services

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Smart Communities for Civic Engagement and
Participation
• Technology can empower citizens to actively engage in
decision-making processes, participate in deliberative
processes, and contribute to societal welfare.
• Online platforms can facilitate community-driven
initiatives, crowdsource ideas for social change, and
enable collaboration between citizens and government
institutions.

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Co-production
• Co-production refers to the collaborative and participatory approach in
which service providers and consumers work together to design,
deliver, and evaluate services and initiatives.
– Co-production in smart communities promotes behavior change by
empowering citizens, designing user-centric solutions, leveraging local
knowledge, fostering collaboration, and facilitating capacity building.

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Towards more citizen-centric and sustainable public services
• The INTERLINK H2020 project aims to overcome the barriers that hinder
administrations to reuse and share services with private partners (including
citizens) by combining the advantages of two often opposed approaches:
– “top-down” approach where Government holds primary responsibility for creating these
services compliant with EU directives, sometimes seeking the support of citizens for
specific design or delivery tasks
– “bottom-up” approach in which citizens self-organize and deliver grassroot services
where government plays no active role in day-to-day activities but may provide a
facilitating framework

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INTERLINK design goals
• COLLABORATION & RE-USE
▪ The INTERLINK platform offers a digital environment that facilitates
co-production processes between Public Administrations, private
stakeholders and citizens and promotes the re-use of software for
delivery of public services.
• CO-DESIGN & CO-DELIVERY
▪ INTERLINK provides a step-by-step guidance for the co-production
and co-delivery of public services along with guidelines, tips and
templates that facilitate the collaboration of different actors.
• INTERLINKERs
▪ Pieces of knowledge or software that your team can re-use and
customize to deliver services.

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INTERLINK co-production methodology

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Co-producers: quadruple helix & valorisation
learning opportunity,
social activity,
public respect, …
new revenue,
product marketing,
corportate image,
partnerships, …
improved processes,
cost savings,
less to do, …
research topics,
project funding,
publications, …
better services,
improved life,
good-will, …
Co-production
Team

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INTERLINK’s Collaborative Environment

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Examples of co-production
• Ministry of Environmental Protection and Regional Development – Latvia: co-
design of e-service description template and co-creation of unified improved e-
service descriptions.
• Council of Zaragoza – Spain: smart logistics, citizen science experientation on Air
Quality or co-design of summer camps for children
• Ministry of Economy and Finance – Italy : Organize and execute the elaboration of
PSPM mock-up from the originated blueprint in iteration 1

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Research challenges
• Make more widely accesible co-production for all:
– Flexible co-production models for different purposes (one-size-fits-all
not possible)
– Recommender of enablers (INTERLINKERs) software and knowledge
ones, based on contents and taxonomy of problem domains
– Underway LLM enabler to further support users in their co-creation
duties

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Citizen Science (CS) for Societal Behavioural
Transformation

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Smart Communities for Data-Driven Policy and
Planning
• Technological advancements, including data analytics and
artificial intelligence, can inform evidence-based
policymaking and planning processes.
• By analyzing large volumes of data, policymakers can gain
insights into societal needs, identify gaps in social
welfare systems, and design targeted interventions.

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Citizen Science
• Citizen science refers to the participation of community members in
scientific research and data collection processes, thereby enabling
them to contribute to scientific knowledge and decision-making.
– Promoting awareness, empowering individuals, and facilitating collaboration,
citizen science projects can inspire and enable behavior change that leads to
more sustainable, responsible, and community-oriented actions.
• Citizen science can be a powerful tool to drive behavior change within smart communities

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SOCIO-BEE: Wearables and droneS fOr CIty Socio-
Environmental Observations and BEhavioral ChangE

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SOCIO-BEE’s mission
24
• Αir pollution is one of the key threats for the
inhabitants of many European cities (~340 million
people)
• Reducing air pollution requires:
• Technological innovation &
• A change in behaviour
• Such changes require collaboration between:
• Citizens
• Businesses
• Volunteers
• Decision makers
The behavioral change due to COVID-19
pandemic, showed a change in energy
demand patterns & a 17% drop in CO2
emissions during the lockdown due to the
reduced use of cars, trucks and buses.
Behavioral change
Awareness raising
Policy shaping

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What are we doing?
25
We kickstart the process
Recruit volunteers
Create Hives
Run Campaigns
Make Change happen!

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How does it work?
Become part of a
volunteer group (we
call Hives)
Use your data for:
- changing your behaviour
- teaching about Air Quality
- changing policies
Use app and sensor to
measure ‘cells’ in your
area
Define an area

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How does it work?

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Citizen Observatory (CO) enabling technology
• GREENGAGE platform for thematic exploitation of co-created urban analytics
pipelines:
▪ Tailored for piloting: That it, to extend/adapt/deploy the technology tools for the selected use
cases including a future re-use in other cities
▪ To prepare user guides/training material for users, apply data quality, validation and FAIR
principles while establishing the connection with GEOSS/open data portals.
• New generation of Citizen Observatories (CO):
▪ Based on equitable collaboration and co-creation of solutions for observing the environment
including citizen observers, professional scientists, and public authorities as equal players in
addressing the socio-ecological challenges for agenda setting and policy shaping.
▪ They will promote innovative governance and help public authorities in shaping their climate
mitigation and adaptation policies by engaging with citizens to co-create and co-exploit green
initiatives focusing on mobility, air quality and healthy living in carbon neutral neighborhoods.

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Citizen observatories (COs) generating data workflows producing
interpretable & actionable knowledge for policy making
• Set of technological assets to enable data value chains through CO activities targeted at urban policy
design and validation:
• Combining authoritative data (Copernicus data plus pilots’ sensor network data) with crowdsourced data
(provided by citizens through portable sensors) to give place to improved models that help governance processes,
decision-making and policy design and validation

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CO enabling technology testing
• Where?
• Turano-Gerace, Bristol,
Copenhagen, Noord Brabant
• Two iterations of piloting:
• Exploratory stage (M13-M18) –
Jan-June 24
• Consolidation stage (M19-M28)
– July24-Apr25

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GREEN Engine
Suite of tools covering the whole
user journey, i.e. from co-designing
CO campaigns which produce
Thematic Explorations to delivering
evidences for policy making or
validating with metrics and results
already designed policies.
CO campaign co-design –
team assembly &
campaign co-specification
(Collaborative
Environment)
CO campaign co-design –
data workflow (capture,
curation, analysis) co-
design with VISAT and
GREEN Toolbox
CO campaign co-delivery:
execution, monitoring and
evaluation (sensors,
GISAT,HotCity, MindEarth
Digital Twin)
CO-campaign co-delivery:
communicate (Academy),
exploit (new policies,
WhiteBook) & sustain
results

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GREEN Engine and toolbox
• GREEN Engine components for Community and Co-production management:
1. Wordpress + Discourse for community management (video how to use)
2. Collaborative Environment for process management
3. Academe with Knowledge Assets (Catalogue)
• GREEN Engine components for Data Mining (capture, curation, visualization and analysis):
1. Data Sources: DataHub can ingest data from more than 50+ sources, including MariaDB/MySQL,
Apache Druid, MongoDB, MS SQL Server, OpenAPI compliant APIs, NiFi...
2. Transformation and processing: DataHub is integrated with tools such as Apache Spark, NiFi and
Hive to transform the data.
3. Visualization and analysis: DataHub is integrated with visualization tools such as SuperSet,
Metabase, PowerBI, Tableau or Great Expectations (tool to evaluate data quality).
4. IDS Connector for data sharing among different pilots
Video demonstrating integration:
DEMO_GreengageDatamanager.mp4

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Research Challenges
• Make more widely accessible Citizen Science for all:
– Crowdsourcing organization and management is tough
• Micro-volunteering engine + Flexible Strategies’ Gamification Engine
– Creating a Citizen Observatory based on low quality data is complex
• Requires guidance in the form of novel co-production processes and enablers
to help citizens take part in data analysis tasks
– Behaviour change demands higher awareness and better
communication of the impact of our actions
– Policy making has to be informed by data evidences

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Conclusions
• Large Techo Social Systems (LTSS) should result in the emergence of
Social Welfare Techno Systems
– Society needs to co-exist with technology and vice versa
– Technology has to empower society
– Society has to trust technology
– Reciprocal understanding and collaboration between AI and people needed
• Co-production and Citizen Science are two novel social collaboration
approaches which combined with disruptive Human-centric
technologies (IoT, HitL AI) realize HumanComp!

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Humanized Computing: the path towards
higher collaboration and reciprocal learning
between machines and people
Dalian University of Technology, Dalian, 29th February 2024
Prof. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es
@dipina