Contact person: Andreas Theodorou (andreas.theodorou@upc.edu) 

Internal Partners:

1. Umeå University (UmU), Andreas Theodorou, andreas.theodorou@umu.se

External Partners:

1. University of Bergen (UiB), Marija Slavkovik, marija.slavkovik@uib.no
2. Open University of Cyprus (OUC), Loizos Michael, loizos@ouc.ac.cy

The right to contest a decision that has consequences on individuals or the society is a well-established democratic right. In the European Union, the General Data Protection Regulation explicitly requires the means of contesting decisions made by algorithmic systems. Contesting a decision is not a matter of simply providing explanation, but rather of assessing whether the decision and the explanation are permissible against an externally provided policy. Albeit its importance, little fundamental work has been done on developing the means for effectively contesting decisions. In this micro-project, we develop the foundations needed to integrate the contestability of decisions based on socio-ethical policy (e.g. Guidelines for Trustworthy Artificial Intelligence (AI)) into the decision-making system. This microproject will lay the basis for a line of research in contestability of algorithmic decision making by considering the overall ethical socio-legal aspects discussed in WP5 of the HumanE-AI-Net project. During the course of this microproject, we will achieve 3 objectives: 1) extend our work on formal language for socio-ethical values, concretised as norms and requirements; 2) conceptualise our feedback architecture which will monitor the predictions and decisions made by an AI system, check the predictions against a policy; and 3) a logic to evaluate black-box prediction based on formal socio-technical requirements by extending our previous work on monitoring and assessing decisions made by autonomous agents. The end result is an agent architecture which contains 4 core components: i) a predictor component, e.g. a neural network, able to produce recommendations for a course of action; ii) a decision-making component, which decides if and which action the agent should take; iii) a utility component, influencing the decision-making component by ascribing a utility value to a potential action to be taken; and iv) a ‘governor’ component; able to reason and suggest the acceptance or rejection of recommendations made by a predictor component. During the microproject, we focus on compliance checking but ensure our architecture is flexible and modular enough to facilitate extensions such as the governor component offering feedback for ‘retraining’ to the predictor component.

Results Summary

We have developed a framework aimed at facilitating appeals against the opaque operations of AI models, drawing on foundational work in contestable AI and adhering to regulatory mandates such as the General Data Protection Regulation (GDPR), which grants individuals the right to contest solely automated decisions. The aim is to extend the discourse on socio-ethical values in AI by conceptualizing a feedback architecture that monitors AI decisions and evaluates them against formal socio-technical requirements. Our results include a proposal for an appeal process and an argumentation model that supports reasoning with justifications and explanations, thereby enhancing the contestability of AI systems. Our work not only advances the theoretical foundations of contestable AI but also proposes practical steps towards implementing systems that respect individuals’ rights to challenge and understand AI decisions. The project has written a draft paper with the aim of submitting it to AAMAS’ blue-sky track.

Contact person: Mireille Hildebrandt (m.hildebrandt@cs.ru.nl) 

Internal Partners:

1. University of Sussex (UOS)
2. German Research Centre for Artificial Intelligence (DFKI)
3. Vrije Universiteit Brussel (VUB)  

HumanE-AI research needs data to advance. Often, researchers struggle to progress because of the lack of data. At the same time, collecting a rich and accurate dataset is no easy task. Therefore, we propose to share through the AI4EU platform the datasets already collected so far by different research groups. The datasets are curated to be ready-to-use for researchers. Possible extension and variation of such datasets are also generated using artificial techniques and published on the platform. A performance baseline is provided for each dataset, in the form of publication reference, developed model or written documentation. The relevant legal framework will be investigated with specific attention to privacy and data protection, as to highlight limitations and challenges for the use and extension of existing datasets as well as future data collection on the subject of multimodal data collection for perception modelling.

Results Summary

There were 2 main outputs:

— Datasets: The partners involved have created, curated and released datasets for Human Activity Recognition (HAR) tasks, in particular, the extended dataset OPPORTUNITY++ and Wearlab BeachVolleyball dataset. The participation in the microproject has offered the chance to get a closer look at the practices, doubts and difficulties emerging within the scientific community involved in the creation, curation and dissemination of training datasets. Considering that one of the goals of the HumanE-AI Net is to connect research with relevant use cases in European society and industry, the participation to the microproject has offered the occasion to situate dataset collection, curation, and release within the broader context of AI pipeline.

— A comprehensive report introducing the concept of “Legal Protection Debt”: the Report examines the potential issues that arise within current ML-practices and provides an analysis of the relevant normative frameworks that govern such practices. By bridging the gap between practices and legal norms, the Report provides researchers with the tools to assess the risks to fundamental rights and freedoms that may occur due to the implementation of AI research in real world situations and recommends a set of mitigating measures to reduce infringements and to prevent violations.

The Report acknowledges that datasets constitute the backbone infrastructure underpinning the development of Machine Learning. The datasets that are created, curated and disseminated by ML practitioners provide the data to train ML models and the benchmarks to test the improvement of such models in performing the tasks for which they are intended.

However, until recently, the practices, processes and interactions that take place further upstream the ML-pipeline, between the collection of data and the use of dataset for training ML-models, have tended to fade into the background.

The report argues that the practices of dataset creation, curation and dissemination play a crucial role in the setting of the level of legal protection that is afforded to all the legal subjects that are located downstream ML-pipelines. Where such practices lack appropriate legal safeguards, a “Legal Protection Debt” can mount up incrementally along the stages of MLpipelines.

In section 1.1., the Report provides a brief overview of how current data science

practices depend on and perpetuate an ecosystem characterised by a lack of structural safeguards for the risks posed by data processing. This can lead to the accumulation of “technical debt”. Such debt, in turn, can assume relevance in the perspective of compliance with legal requirements. Taking inspiration from the literature on technical and ethical debt, the Report introduces the concept of Legal Protection Debt. Because of this legal protection debt, data-driven systems implemented at the end of the ML pipeline may lack the safeguards necessary to avoid downstream harm to natural persons.

The Report argues that the coming about of Legal Protection Debt and its accumulation at the end of the ML pipeline can be contrasted through the adoption of a Legal protection by design approach. This implies the overcoming of a siloed understanding of legal liability that mirrors the modular character of ML pipelines. Addressing legal protection debt requires ML practitioners to adopt a forward looking perspective. Such perspective should situates the stage of development in practitioners are involved in the context of the further stages that take place both upstream and downstream the pipeline. The consideration of the downstream stages of the ML-pipeline shall, as it were, back propagate and inform the

choices as to the technical and organisational measure to be taken upstream: upstream design decisions must be based on the anticipation of the downstream uses afforded by datasets and the potential harms that the latter may cause. Translated into a legal perspective, this implies that the actors upstream the pipeline should take into consideration the legal requirements that apply to the last stages of the pipeline.

The Report illustrates how data protection law lays down a set of legal equirements that overcome modularity and encompass the ML pipeline in its entirety, connecting the actors upstream with those downstream. The GDPR makes controllers responsible for the effects of the processing that they carry out. In section 2, the Report shows how the GDPR provides the tools to mitigate the problem of many hands in ML-pipelines. The duties and obligations set by the GDPR require controllers to implement by design safeguards that conjugate the

need to address downstream harms with the necessity to comply with the standards that govern scientific research. In this perspective, the Report shows that the obligations established by data protection law either instantiate or harden most of the requirements set by the Open science and Open data framework and also the best practices emerging within the ML-community.

In section 2.1. the report illustrates the core structure of the regime of liability to which controllers are subject under the GDPR. Such a regime of liability hinges upon controllers’ duty to perform a context-dependent judgment. Such judgment must inform controllers’ decisions as to the measures to be adopted to ensure compliance with all the obligations established by the GDPR. Such judgment must be based on the consideration of the downstream harms posed by the processing.

In essence, the duty to anticipate and address potential downstream harms requires controllers to adopt a forward-looking approach. In order to ensure compliance with the GDPR, controllers must engage in a dynamic, recursive practice that addresses the requirements of present processing in the light of the future potential developments. At the same time, the planning effort required by the GDPR is strictly connected with the compliance with obligations set by other normative frameworks. In this sense, compliance with the GDPR and compliance with obligations such as those imposed by the Open science and Open data framework go hand in hand. Compliance with the GDPR is a pre-requisite

for complying with Open science and Open data framework. Simultaneously, the perspective of open access and re-usability of datasets affects the content of the obligations set by the GDPR.

As a result, the consideration of “what happens downstream” – i.e., the potential uses of datasets, potential harms that the latter may cause, further requirements imposed by other normative frameworks – back propagates, determining the requirements that apply upstream.

In section 2.2. we show how the compliance with the documentation obligations set by the GDPR can contrast the accumulation of a documentation debt and ensure controllers’ compliance with the obligations established by other normative frameworks, such as Open Data and Open Science. The overlapping between the documentation requirements established by such different frameworks shows firstly that a serious approach to the compliance with the GDPR can provide the safeguards necessary to contrast the accumulation of a documentation debt. In this way, compliance with the documentation obligations set by the GDPR can prevent the accumulation of other forms of technical debt and, eventually, of legal protection debt. At the same time, the convergence between the requirements set by the GDPR and those established by the FAIR principle and the Horizon DMP template shows how the performance of the documentation obligations established by the GDPR can also facilitate compliance with requirements specific to data processing conducted in the context of scientific research.

A correct framing of the practices of dataset creation, curation and release in the context of research requires to make an effort towards the integrity of the legal framework as a whole, taking into consideration the relations between Open data, Open science and data protection law. First, it is first important to stress that compliance with data protection law represents a pre-requisite for the achievement of the goals of Open Data and Open Science framework.

In section 2.3. the report analyses the requirements that govern the release and downstream (re)use of datasets. Compliance with the requirements set by the GDPR is essential to avoid that dataset dissemination gives rise to the accumulation of legal protection debt along ML pipelines. Based on the assessment of adequacy and effectiveness required for all forms of processing, controllers can consider the adoption a range of measures to ensure that data

transfer are compliant with the GDPR. Among such measures, the Report examines the use of licenses, the providing of adequate documentation for the released dataset, data access management and traceability measures, including the use of unique identifiers.

The Report contains an Annex illustrating the provisions of the GDPR that establish a special regime for the processing carried out for scientific research purposes. We highlight how most of the provisions contained in the GDPR are not subject to any derogation or exemption in view of the scientific research purpose of the processing. All in all, the research regime provided by the GDPR covers the application of a limited number of provisions (or part of provisions). A process that is unlawful in that it does not comply with the general provisions set by the GDPR cannot enjoy the effects of the derogations provided by the research regime. The derogations allowed under the special research regime concern

almost exclusively the GDPR provisions on the rights of data subjects, while no derogation is possible for the general obligations that delineate the responsibility of the controller. The derogations provided under the special research regime allow controllers to modulate their obligations towards data subjects where the processing of personal data is not likely to affect significantly the natural persons that are identified or identifiable through such data. As it were, the decrease of the level of potential harm makes possible the lessening of the safeguards required to ensure the protection of data subjects. Even in such cases, however, no derogation is allowed with respect to the requirements different than those concerning the rights of the data subject. This circumstance makes manifest that the system established by the GDPR aims at providing a form of protection that goes beyond the natural persons whose personal data are processed at that time by controllers.

Contact person: Joao Gama ( jgama@fep.up.pt) 

Internal Partners:

1. INESC TEC, Joao Gama
2. Università di Pisa (UNIPI), Dino Pedreschi
3. Consiglio Nazionale delle Ricerche (CNR), Fosca Giannotti  

Nowadays ML models are used in decision-making processes in real-world problems by learning a function that maps the observed features with the decision outcomes. However, these models usually do not convey causal information about the association in observational data, thus not being easily understandable for the average user, therefore not being possible to retrace the models’ steps, nor rely on its reasoning. Hence, it is natural to investigate more explainable methodologies, such as causal discovery approaches, since they apply processes that mimic human reasoning. For this reason, we propose the usage of such methodologies to create more explicable models that replicate human thinking, and that are easier for the average user to understand. More specifically, we suggest its application in methods such as decision trees and random forest, since by themselves are highly explainable correlation-based methods.

Results Summary

In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. The result of this project is a survey aiming at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. The published paper containts sections covering the following items. In Section 2, some basic definitions and notations are introduced. In Section 3, causal discovery techniques, tools, datasets, metrics, and examples are presented, organized by data type (cross-sectional, time-series, longitudinal). Section 4 covers causal inference techniques for several causal effects, tools, datasets, and a running example. Some remarks regarding the intersection between ML and causality are presented in Section 5, where some of the current open issues are also highlighted. Finally, conclusions are drawn.

Tangible Outcomes

1. Nogueira, Ana Rita, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, and João Gama. “Methods and tools for causal discovery and causal inference.” Wiley interdisciplinary reviews: data mining and knowledge discovery 12, no. 2 (2022): e1449. https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1449
2. Github repository of datasets, and papers related to causal discovery and causal inference research https://github.com/AnaRitaNogueira/Methods-and-Tools-for-Causal-Discovery-and-Causal-Inference
3. Github repository of software related to causal discovery and causal inference research https://github.com/AnaRitaNogueira/Methods-and-Tools-for-Causal-Discovery-and-Causal-Inference

Contact person: Nina Khairova (nina.khairova@umu.se) 

Internal Partners:

1. Umea University, Nina Khairova, nina.khairova@umu.se

External Partners:

1. Gdansk University of Technology, Nina Rizun, nina.rizun@pg.edu.pl
2. University of the Aegean, Charalampos Alexopoulos, alexop@aegean.g   

Currently, almost all government, commercial, and non-profit organizations actively use social media for the dissemination of information and communication with citizens. Social media should serve to enhance citizen engagement and trust, contribute to the improvement of government institutions’ transparency, and guarantee freedom of speech and expression. However, the government needs to be aware of and mitigate the risks associated with the use of social media. One of the most significant is the risk of spreading misinformation that has become increasingly prevalent and easily accessible to the global social media audience.

In general, the government and public officials’ social media accounts are trustworthy and aim to disseminate high-quality and timely information to the public ensuring its reliability, integrity, accessibility, and validity. However, non-compliance with the rules of effective and trusted two-way communication on public officials’ accounts, untimely updating of the communication channel, and incomplete responses to user comments could lead to a tendency of citizens to search (or check) the information in other social media sources. Such information sources include traditional news outlets, professional or casual journalists, or ordinary users. Wherein the risk of misinformation being disseminated could undermine citizens’ trust in the government, as well as threaten the security and privacy of both official government data and personal data. Moreover, the sharing of inaccurate and misleading information could lead to significant social consequences, such as the exacerbation of social inequalities, the creation of social divisions between different social groups, and the manipulation of their opinions.

In our microproject, we strive to develop an actionable AI-based approach to objectively assess information trustworthiness on social media based on the combination of AI algorithms, such as unsupervised machine learning, text analytics, and event argument extraction. We apply our approach to the analysis of textual information in Polish, Ukrainian, and English thematically related to the main ethical, legal, and societal issues caused by the migration of Ukrainians to European countries as a result of the ongoing Russian invasion. The choice of migration crisis domain to assess the reliability of social media information is due to the following reasons. First, as recent research demonstrates, migration issues are among the most hotly debated on social media and can be especially subjected to attempts of various kinds of disinformation. Second, the migration problem includes a lot of associated issues such as ethical issues (e.g., vulnerable to exploitation by employers, low wages, work in unsafe conditions, discrimination, and marginalization), legal issues (e.g., immigration laws and policies, visa regulations and border controls, limited access to justice), social security (e.g., social protection, mental support, integration, language barriers, and cultural differences, social isolation), and the problems of reporting to the public the appropriate policies to support Ukrainian migrants in their countries of destination and to address the root causes of migration in Ukraine itself. The official and unofficial information on social media that covers all these issues are supposed to be considered in the microproject.

Therefore, the development of an actionable AI-based approach for determining information trustworthiness (i) will serve to expand understanding of the core information needs of citizens in communication with the government in the context of migration issues in the last year; and potential causes and nature of that information untrustworthiness in social media; and (ii) can support the government to develop relevant guidelines to oversee social media, and instruments to assess, analyze and monitor implementation and compliance with ELS principles in social media.

Results Summary

Using a text analytics approach such as BERTopic topic modeling, we analyzed text messages published on Telegram channels from February 2022 to September 2023, revealing 12 challenges facing Ukrainian migrants. Furthermore, our study delves into these challenges distribution across 6 major European countries with significant migrant populations, providing insights into regional differences. Additionally, temporal changes in 8 narrative themes in discussions of Ukrainian migration, extracted from official government websites, were examined. Together, this research contributes (1) to demonstrating how analytics-driven methodology can potentially be used to extract in-depth knowledge from textual data freely available on social media; and (2) to a deeper understanding of the various issues affecting the adaptation of Ukrainian migrants in European countries. The study also provides recommendations to improve programs and policies to better.  

Tangible Outcomes

1. Nina Khairova, Nina Rizun, Charalampos Alexopoulos, Magdalena Ciesielska, Arsenii Lukashevskyi, Ivan Redozub/Understanding the Ukrainian Migrants Challenges in the EU: A Topic Modeling Approach/Proceedings of the 25th Annual International Conference on Digital Government Research, 2024, 196-205 p. https://dl.acm.org/doi/abs/10.1145/3657054.3657252
2. The SOMTUME dataset contains textual information gathered from social media and news sites, comprising two segments: Trustworthiness Information Content (TIC) and Uncertain Information Content (UIC). The texts pertain to the migration of Ukrainians to the European Union from February 2022,  to August 2023. https://github.com/ninakhairova/SOMTUME
3. The results “Understanding the Ukrainian Migrants’ Challenges in the EU: A Topic Modeling Approach” of the microproject  were presented at the 25th Annual International Conference on Digital Government Research (dg.o 2024), held from June 11–14, 2024, at National Taiwan University in Taipei, Taiwan. https://dgsociety.org/dgo-2024/program/

Contact person: Dilhan Thilakarathne (dilhan.thilakarathne@ing.com)

Internal Partners:

1. ING Groep NV, Dilhan Thilakarathne
2. Umeå University (UMU), Andrea Aler Tubella  

After choosing a formal definition of fairness (we limit ourselves with definitions based on group fairness through equal resources or equal opportunities), one can attain fairness on the basis of this definition in two ways: directly incorporating the chosen definition into the algorithm through in-processing (as another constraint besides the usual error minimization; or using adversarial learning etc.) or introducing an additional layer to the pipeline through post-processing (considering the model as a black-box and focusing on its inputs and predictions to alter the decision boundary approximating the ideal fair outcomes, e.g. using a Glass-Box methodology).

We aim to compare both approaches, providing guidance on how best to incorporate fairness definitions into the design pipeline, focusing on the following research questions: Is there any qualitative difference between fairness acquired through in-processing and fairness attained by post-processing? What are the advantages of each method (e.g. performance, amenability to different fairness definitions)?

Results Summary

The work focuses on the choice between in-processing and post-processing showing that it is not value-free, as it has serious implications in terms of who will be affected by a fairness intervention. The work suggests how the translation of technical engineering questions into ethical decisions can concretely contribute to the design of fair models and the societal discussions around it.

The results of the experimental study provide evidences that are robust w.r.t. different implementations and discuss it for the case of a credit risk application. The results demonstrate how the translation of technical engineering questions into ethical decisions can concretely contribute to the design of fair models. At the same time, assessing the impacts of the resulting classification can have implications for the specific context of the original problem. We summarize our results in a paper addressing the difference between in-vs-post processing methods on ML models focusing on fairness vs performance trade-offs.

Tangible Outcomes

1.  Ethical implications of fairness interventions: what might be hidden behind engineering choices?– Andrea Aler Tubella, Flavia Barsotti, Ruya Gokhan Kocer, Julian Alfredo Mendez
   https://doi.org/10.1007/s10676-022-09636-z
2. Video presentation summarizing the project

Contact person: Andrea Galassi (a.galassi@unibo.it) 

Internal Partners:

1. University of Bologna, Andrea Galassi, a.galasi@unibo.it

External Partners:

1. University of Calabria, Bettina Fazzinga, bettina.fazzinga@unical.it
2. University of Naples, Margherita Vestoso, margherita.vestoso@unina.it

Migration is one of the top current concerns of the European Union that requires harmonizing ethical, legal and societal considerations. Unfortunately, the application rules and laws concerning immigration and asylum require a nontrivial evaluation of legal and factual facts, which makes it difficult for migrants to achieve a preliminary overview of their chances to obtain protection. In this micro-project, we explored how trustworthy AI can play a role in making asylum application processes more efficient and fair. To this end, we gathered a multidisciplinary team of computer scientists and immigration law experts. The team worked towards the creation of a chatbot aimed at supporting migrants who seek asylum in Europe. In particular, we developed a prototypical tool to support, inform, and guide asylum applicants in the process (and not to assist or replace a judiciary expert in a “predictive justice” fashion). We started from a previous micro project (“Ethical Chatbots”, Fazzinga, B., Galassi, A., and Torroni, P. (2022). A privacy-preserving dialogue system based on argumentation. Intelligent Systems with Applications, 16, 200113) and improved our argumentative chatbot architecture to address the complexity of the new domain and to exploit the power of LLMs. We maintained the focus on properties such as data governance and privacy preservation, transparency, explainability, and auditability.

Our experience highlighted the necessity of developing this kind of tool in close relationships with domain experts. The tool development process demonstrated that, besides the knowledge of the relevant legal framework, a crucial role is played by a set of unwritten best practices and conventions, often not explicitly represented anywhere, and mostly known by practitioners through experience. Moreover, since it is extremely difficult to have access to the asylum request and the corresponding court decisions are not shared publicly, for the sake of applicants’ safety, a data-oriented approach is not feasible, and neither do current LLMs, trained on available data, have the necessary information for providing meaningful answers.

Results Summary

We developed “ACME”, a prototype chatbot with the aim of supporting migrants in their requests for asylum. ACME is a hybrid architecture that combines a subsymbolic language understanding module based on NLP techniques and LLM, with a symbolic reasoning module based on computational argumentation.

The aim of the tool is to help migrants identify the highest level of protection they can apply for. This would contribute to a more sustainable migration by reducing the load on territorial commissions, Courts, and humanitarian organizations supporting asylum applicants.

Relevant properties ACME exhibits include: data governance and privacy thanks to its modular architecture; transparency and explainability thanks to argumentative reasoning; and the ability to integrate and reasoning with explicit, expert-made, formalized knowledge, ensuring auditability.

Tangible Outcomes

1. Bettina Fazzinga, Elena Palmieri, Margherita Vestoso, Luca Bolognini, Andrea Galassi, Filippo Furfaro, Paolo Torroni (2024). “A Chatbot for Asylum-Seeking Migrants in Europe”. IEEE International Conference on Tools with Artificial Intelligence (ICTAI) http://arxiv.org/abs/2407.09197 
2. chatbot code https://github.com/lt-nlp-lab-unibo/ACME-A-Chatbot-for-Migrants-in-Europe 
3. Video demonstration of the tool: https://www.youtube.com/watch?v=P8iW7FOZTYM&feature=youtu.be 

Contact person: Laura Sartori, (l.sartori@unibo.it)

Internal Partners:

1. Università degli studi di Bologna (UNIBO), Laura Sartori, l.sartori@unibo.it
2. Umeå University (UMU)
3. Consiglio Nazionale delle Ricerche (CNR)

We want to conduct empirical research that explores the social and public attitudes of individuals towards AI and robots. AI and robots will enter many more aspects of our daily life than the average citizen is aware of while they are already organizing specific domains such as work, health, security, politics and manufacturing. Along with technological research it is fundamental to grasp and gauge the social implications of these processes and their acceptance into a wider audience.

Some of the research questions are:

1. Do citizens have a positive or negative attitude about the impact of Ai?
2. Will they really trust a driverless car, or will they passively accept a loan or insurance’s denial based on an algorithmic decision? Do states alone have the right and expertise to regulate the emerging technology and digital infrastructures? What about technology governance?
3. What are the dominant AI’s narratives in the general public?

Results Summary

The Bologna survey collected around 6000 questionnaires. Data analysis on the Bologna case study revealed a quite articulated picture where variables such as gender, generation and competence resulted crucial in the different understanding and knowledge about AI.

AI Narratives sensibly vary across social groups, underlying a different degree of awareness and social acceptance. The UMEA and CNR surveys had more problems in the collection phase, while the implementation and launch of the surveys were smooth and on time.

Tangible Outcomes

1. “A sociotechnical perspective for the future of AI: narratives, inequalities, and human control, in Ethics and Information technology”. L. Sartori, Laura, A. Theodorou. Published in Ethics and Information Technology 24.1 (2022) https://link.springer.com/ https://link.springer.com/article/10.1007/s10676-022-09624-3
2.  Sartori, Laura, and Giulia Bocca. “Minding the gap (s): public perceptions of AI and socio-technical imaginaries.” AI & society 38.2 (2023): 443-458. https://philpapers.org/rec/SARMTG 
3. slides: https://www.humane-ai.eu/_micro-projects/mps/MP-17/UNIBO_sartori_What%20idea%20of%20AI_141021_Berlin.pptx 

Contact person: John Shawe-Taylor (j.shawe-taylor@ucl.ac.uk)

Internal Partners:

1. Knowledge 4 All Foundation, Davor Orlic, davor.orlic@gmail.com
2. University College London, John Shawe-Taylor, j.shawe-taylor@ucl.ac.uk
3. Institut Jožef Stefan, Davor Orlic and Marko Grobelnik, davor.orlic@gmail.com

K4A proposed a microproject to extend its existing prototype of the online learning platform X5LEARN (https://x5learn.org/) developed by K4A and UCL and JSI and its new IRCAI center under the auspices of UNESCO. It is a standalone, learner-facing web application designed to give access through an innovative interface to a portfolio of openly licensed educational resources (OER) in video and textual format. Designed for lifelong learners looking for specific content wanting to expand on their knowledge, our aim is to extend it to AI-related topics. The updated application will be released via IRCAI, a newly designated AI center and integrated with AI4EU with heavy HumaneAI branding. The main reason to push the product with IRCAI is that UNESCO is positioning itself as the main UN agency to promote humanist Artificial Intelligence, a major international policy on the Ethics of AI, and champion OER, which is in line with HumaneAI.

Results Summary

Under this microproject, a series of extensions to the X5Learn platform was added. A new user friendly user interface was developed and deployed. X5Learn, being an intelligent learning platform, a series of human-centric AI technologies that enable educational recommendation, intelligent previewing of information and scalable question generation that can help different stakeholders such as teachers and learners were developed backed by scientific research. The results have been published in peer reviewed conferences such as AAAI, AIED and CHIIR and also published in the Journal of Sustainability. The new earning platform is now available to the public including a python library that implements the recommendation algorithms developed.

Tangible Outcomes

1. Maria Pérez Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, and Emine Yilmaz. 2022. Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval (CHIIR ’22). Association for Computing Machinery, New York, NY, USA, 90–101. https://doi.org/10.1145/3498366.3505814 
2. Maria Perez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela, Stefan Kreitmayer, Emine Yilmaz, Richard Noss, and John Shawe-Taylor. 2021. X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. In 26th International Conference on Intelligent User Interfaces – Companion (IUI ’21 Companion). Association for Computing Machinery, New York, NY, USA, 70–74. https://doi.org/10.1145/3397482.3450721 
3. Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, and John Shawe-Taylor. 2022. Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources. Sustainability 14, 18: 11682. https://doi.org/10.3390/su141811682 
4. [arxiv] Bulathwela, S., Pérez-Ortiz, M., Holloway, C., & Shawe-Taylor, J. (2021). Could ai democratise education? socio-technical imaginaries of an edtech revolution. arXiv preprint arXiv:2112.02034.https://arxiv.org/abs/2112.02034 
5.  X5Learn Platform: https://x5learn.org/ 
6.  TrueLearn Codebase: https://github.com/sahanbull/TrueLearn 
7.  TrueLearn Python library: https://truelearn.readthedocs.io 
8.  X5Learn Demo Video: https://youtu.be/aXGL05kbzyg 
9.  Longer lecture about the topic: https://youtu.be/E11YUWad7Lw 
10.  Workshop presentation (AAAI’21): https://www.youtube.com/watch?v=gYtmL2XdxHg 
11.  Workshop Presentation (AAAI’21): https://youtu.be/4v-fizLvHwA 

Contact person: Rui Prada (rui.prada@tecnico.ulisboa.pt)

Internal Partners:

1. IST, Rui Prada, rui.prada@tecnico.ulisboa.pt @tudelft.nl 
2. Umeå University (UmU), Juan Carlos Nieves, jcnieves@cs.umu.s   

External Partners:

1. UPC
2. Bath  

This project aims at developing an explicit representation and interpretation of stakeholders’ socio-ethical values, conventions, and norms, and to incorporate them in AIS component reasoning and decision-making processes. By doing so, we can enable ethics-by-design approaches in which the agents take into consideration the wider socio-ethical values that they are meant to fulfil as part of the socio-ethical systems that they belong to.

There is extensive literature on the formalisation of value systems, norms, and conventions, but most works cover only one level of abstraction at one end of the spectrum – either abstract and universal or concrete and specific to a particular scenario – and in a single interaction context. However, the real social world is much more complex with multiple overlapping contexts that comprise extended sequences of events within contexts, while events can individually and severally have effects across contexts. There are multiple – not fully compatible – value theories, such as Self-Determination Theory or the Schwartz Value System. These are also abstract in nature and not directly applicable to an agent’s behaviour. A key factor in understanding how values affect actions is that preferences over values are context-dependent, so certain values are more important than others according to the circumstances. This becomes more complicated when we consider that an agent can be in more than one context at the same time and thus have to handle different, possibly conflicting, preference orderings. Lastly, there is the mutual interaction of values with context to address: the context affects the value preferences, but also value preferences affect the context. Consequently, to formalise value preferences for agents, we need to identify and define a suitable approximation of a context for the purposes of this microproject.

In this microproject, we develop:

1. A novel agent architecture that allows agents to be aware of the values/norms/conventions for each of the social contexts related to their interactions by creating separate explicit representations for each context, and then utilising these context representations in reasoning and decision making to align the resulting behaviour to the social values of the contexts in which the agent is situated.
2. A methodology to develop a multi-level, multi-contextual model to formalise a connection from abstract, universal social values to concrete behaviour of agents in a particular social context. More concretely, we aim to create a computational representation of nested, overlapping (eventually contradictory) social contexts, where the set of values and the preference function over them (and their respective norms and conventions) of a given context are properly derived from abstract values in higher-level, more general (super) contexts, up to universal, abstract values.

Results Summary

An exploratory exercise of values and preferences was carried out on a water consumption scenario.

An agent-based model (ABM) was developed and run, with values and value preferences as part of agents’ deliberation as well as contexts expressed as value preferences. Contexts affected agents in such a way that it may result in them temporarily changing their value preferences according to an effort function (we assumed such effort would be proportional to how much importance each agent gave to their values).

The projects’ outcomes show that:

• Adding value preferences and contexts delivers more realistic results in a water-consumption multi-agent simulation.
• Given our grounding in the Schwartz’s circumflex model of values and a value preference, some value orders are more prone to shift than others, that is, they are more flexible in terms of changing their preferences.

We wrote a workshop paper presenting the results of experimenting with such ABM.

Tangible Outcomes

1. [workshop] Oliva-Felipe, L., Lobo, I., McKinlay, J., Dignum, F., De Vos, M., Cortés, U., Cortés, A. (2024). Context Matters: Contextual Value-Based Deliberation in Water Consumption Scenarios. In: XXX, Y., et al. Artificial Intelligence. ECAI 2024 International Workshops. ECAI 2024. Communications in Computer and Information Science, vol XXXX. Springer, Cham. https://doi.org/XXXX/YYYYY (accepted, to be printed)
2. A software implementing the above-mentioned model. The code of the ABM can be found in this repository: https://github.com/HPAI-BSC/value-based-water-consumption/

Contact person: Francesco Spinnato Riccardo Guidotti (francesco.spinnato@sns.it)

Internal Partners:

1. Generali Italia
2. CNR Pisa
3. Università di Pisa

For insurance business a connected car is a vehicle where an embedded telematics device streams acceleration data, GPS position and other physical parameters of the moving car. This live streaming is used for automatic real time detection of car crashes. The project is focused on the development of an XAI layer which translates the logical outcome of an underneath LSTM used for crash detection into a human readable format.

Results Summary

• Industrial outcome: the LSTM automatic labeling of a signal event from a car telematics box as a ‘crash’ triggers an emergency live call from a contact center to the driver’s phone for health assessment and further help. If the driver is not responding or is out of reach, more actions could follow (e.g. call to emergency service). In order to improve the efficiency of this emergency procedure, is vital for the contact center operator to reduce the number of false positive events (e.g. being able to read the outcome of the box and discriminate a false positive event)
• Societal outcome: an improved efficiency in connected car crash detection (reduction of false positives) can reduce the number of car crashes with fatal or severe injury outcome and also improve road safety.

Tangible Outcomes

1.  Explaining Crash Predictions on Multivariate Time Series Data  The Lecture Notes in Computer Science book series (LNAI,volume 13601) https://link.springer.com/chapter/10.1007/978-3-031-18840-4_39 

Contact person: Joao Gama (INESC TEC) (jgama@fep.up.pt)

Internal Partners:

1. INESC-Tech Joao Gama,
2. CNR, Giuseppe Manco,
3. ULEI, Holger Hoos 

The goal is to devise a data generation methodology that, given a data sample, can approximate the stochastic process that generated it. The methodology can be useful in many contexts where we need to share data while preserving user privacy. There are known literature for data generation based on Bayesian neural networks/hidden Markov models that are restricted to static and propositional data. We focus on time-evolving data and preference data. We will study essentially two aspects: (1) the generator to produce realistic data, having the same properties of the original one, and (2) we want to investigate how to inject drift within the data generation process in a controlled manner. The idea is to model the stochastic process through a dependency graph among random variables so that the drift can be simply modeled by changing the structure of the underlying graph through a morphing process.

Tangible Outcomes

1. available on github – https://github.com/fsp22/mcd_dds4rs 
2. implementation of the model presented in the paper “Modelling Concept Drift in Dynamic Data Streams for Recommender Systems” https://github.com/fsp22/mcd_dds4rs