Giovanni Arras, born in Tempio Pausania (SS) on 18/08/1995 and affected by undefined infantile cerebral palsy. He graduated in history and philosophy from the University of Pisa and with a master's degree from the University of Sassari with an experimental thesis about the cognitive bias of time perception. At the moment, he teaches history and philosophy in Olbia's Liceo Artistico F. De Andrè. He's also involved in AI studies that are based on its ethical role and its possible developments in a specific medical field.

Elena Beani is a paediatric physical therapist, with PhD in Neuroscience, actually employed as Junior researcher at the University of Pisa. Since 2012 she collaborates in the INNOVATE Lab of IRCCS Stella Maris in several research projects. Her area of interest is the use of ICT for clinical assessment and for the home-based telerehabilitation in infants and children with neurological disorders.

New perspectives on the use of wearable sensors in children

Children with Cerebral Palsy often present high impairment in the Upper Limb (UL), resulting in asymmetric movement patterns, causing difficulties in performing bimanual dailylife tasks. The assessment of manual skills, traditionally carried out with clinical assessment tools, has been recently upgraded thanks to the integration of wearable technologies in the clinical assessment. Mainly composed by wearable sensors, this technology has been validated for recording the ULs behavior of children with typical development and Unilateral CP during a standardized clinical evaluation, such as the Assisting Hand Assessment. After the discovery of the correlation between the clinical scores and technological data, the further challenge has been to record the spontaneous behavior during dailylife. For this reason, machine learning algorithms have been applied to develop new technological-related indexes useful for evaluating the individuals’ activities to lead an independent life.

Roslyn Boyd is the Professor of Cerebral Palsy research and an National Health and medical Research Council Fellow at the Queensland Cerebral Palsy and Rehabilitation Research Centre, at the University of Queensland. Her research collaborates with colleagues at CSIRO to utilise machine learning techniques to extract clinically useful information from medical images that could be used to predict patient function and improve outcomes for children with neurological injuries.

Combining Brain structure on MRI and functional outcomes in Cerebral Palsy

Quantitative radiological reporting of brain MRIs provides the opportunity to explore the relationships between brain macro and micro structure and a range of Functional outcomes in children with Cerebral Palsy. In collaboration with our colleagues at CSIRO, we have utilised multiple quantitative radiological tools (sMRI, dMRI, fMRI and rsfMRI) to quantify brain injury, and to examine the relationship to multiple functional outcomes (manual ability of the Assisting Hand Assessment, Gross motor function GMFM; communication and cognition). In this talk, I will present several studies where we have looked cross section outcomes of brain structure on the Fiori semi quantitative scale and multiple functions (Australian CP Child study), some very early examination of brain injury and development in a preterm cohort (sMRI, dMRI with Kiddokorro scale) and evaluation of pre -post brain changes after intensive interventions (Habitile study). We will discuss the potential opportunities and limitations of these studies in children with CP.

Cucchiara is Full professor of “Computer Engineering and Science” (ING-INF/05 «Sistemi di Elaborazione dell’Informazione») within the Dipartimento di Ingegneria “Enzo Ferrari” (DIEF) at the UNIMORE, Università di Modena e Reggio Emilia, Italy. She is Director of the Artificial Intelligence Research and Innovation Center AIRI (ex Softech-ICT)  of the Modena Technopole co-funded by  the Emilia Romagna High Technology Network, under EU FESR programs, an Interdepartmental Center of Research of UNIMORE (www.airi.unimore.it). She is Director of the ELLIS Unit of Modena; ELLIS is the Euoropean Labs of  Learning and Intelligent Systems (www.ellis.eu) and coordinates the Research Lab AImagleab, active in Computer Vision, Pattern Recognition and Multimedia within Dipartimento di Ingegneria “Enzo Ferrari”, comprehending more than 40 researchers and Phd Students and the industrial research activities in AI for UNIMORE (www.AIacademy.unimore.it ). She is Coordinator of the UNIMORE Unit of the National Phd School in AI for Society. Rita Cucchiara in 2016-2018 has been President of the Italian Association of Pattern Recognition, Learning and Computer vision CVPL (www.cvpl.it), affiliated to IAPR, and in 2018-2021 has been Director of the CINI AIIS Lab The Lab of Artificial Intelligence and intelligent Systems of CINI. Rita Cucchiara is since 2015 an Advisory Board Member of the Computer Vision Foundation, CVF as PC of ICCV2017 and GC of CVPR2024 and is in the European Computer Vision Alliance Governing board as GC of ECCV2022; since 2017 in the board of Directors of Italian Institute of Technology. She is now member of Board of Directtors of ART-ER and Prometeia spa. Since 2022 is also affiliated with IIT-CNR.

Challenges in Computer Vision, NLP with Generative AI

Computer Vision and NlP are mostly addressed problems of perception and understanding for two aims: first, to extract knowledge by interacting with the environment and humans ( and the huge human- generated digital content);    second, to reason on  multimodal knowledge either to give  answers to human requests either to take actions with autonomous systems e.g, vheicles, drones and robots. In the last few years the generative AI approaches  have changed the  paradigims: Autoencosers, GANs, Transformers, Diffusion models can create synthetic data in a more and more impressive results. From one side they can be used to improve  perception and understanding,  E.g, by  providing augmented datasets and also  new  methods for  human centric performance measurements.   The examples of including reinforcement learning with human feedback  enriched by synthetic data and semi supervised methods. As the same time they constitute new challenges for trustworthy AI, such as  fake image detection . We will discuss these topics with some technical examples  of approaches of  generative methods   (visual to textual and viceversa), their Impact in computer vision and nlp challenges and in applications as well as the related privacy, ethics and fairness issues.

Patients and people with disabilities rights activist, Francesca co-founded Fightthestroke Foundation together with Roberto D'Angelo, with the aim of developing inclusive solutions for young stroke survivors and people with Cerebral Palsy. Thanks to the use of technology, inclusive design and science, Fightthestroke has revolutionized family engagement in research and the Italian advocacy ecosystem with their solutions, winner of numerous awards for being able to combine the most advanced discoveries in Neuroscience with Artificial Intelligence and community based healthcare. Francesca is the author of the book "Lotta e Sorridi" published by Sperling & Kupfer in 2015 and translated into German by Bastei Lubbe in 2018, she is Ashoka, Eisenhower and Global Good Fund Fellow, TED speaker and TEDMED ambassador in Italy since 2014, board member of IAPS-IPSO.

Silvia Filogna has the Master’s degree in Biomedical Engineering and the PhD in BioRobotics. Currently, she is a Post-doc researcher at the University of Pisa, working as biomedical engineer at IRCCS Fondazione Stella Maris, a research-based children's hospital. Her main research activities are related to the management and analysis of data coming from different technologies (i.e., sensors, robotic platforms and virtual reality environments), also using AI techniques, and to the design of technological systems to be applied in the biomedical field.

New perspectives on the use of wearable sensors in children

Children with Cerebral Palsy often present high impairment in the Upper Limb (UL), resulting in asymmetric movement patterns, causing difficulties in performing bimanual dailylife tasks. The assessment of manual skills, traditionally carried out with clinical assessment tools, has been recently upgraded thanks to the integration of wearable technologies in the clinical assessment. Mainly composed by wearable sensors, this technology has been validated for recording the ULs behavior of children with typical development and Unilateral CP during a standardized clinical evaluation, such as the Assisting Hand Assessment. After the discovery of the correlation between the clinical scores and technological data, the further challenge has been to record the spontaneous behavior during dailylife. For this reason, machine learning algorithms have been applied to develop new technological-related indexes useful for evaluating the individuals’ activities to lead an independent life.

Fosca Giannotti is Full Professor at Scuola Normale Superiore, Pisa, Italy. Fosca Giannotti is a pioneering scientist in mobility data mining, social network analysis and privacy-preserving data mining. Fosca leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR, founded in 1994 as one of the earliest research lab on data mining. Fosca's research focus is on social mining from big data: smart cities, human dynamics, social and economic networks, ethics and trust, diffusion of innovations. She is author of more than 300 papers. She has coordinated tens of European projects and industrial collaborations. Fosca is the former coordinator of SoBigData(link is external), the European research infrastructure on Big Data Analytics and Social Mining, an ecosystem of ten cutting edge European research centres providing an open platform for interdisciplinary data science and data-driven innovation. Recently she became the recipient of a prestigious ERC Advanced Grant entitled XAI – Science and technology for the explanation of AI decision making.

Hybrid Decision Making

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I work in the areas of Cognitive Science and Artificial Intelligence (AI). I studied Philosophy and Psychology at Oxford University, then did postgrad and postdoc work in AI at the University of Edinburgh. I worked at Otago University's Department of Computer Science from 1998 until 2022, and I now work at VUW's School of Engineering and Computer Science. As well as researching AI methods, I am also very interested in the ethics and social impacts of AI. I work in several groups studying these questions, including the Global Partnership for AI, the Christchurch Call, and the Global Internet Forum to Counter Terrorism. I was a co-founder of Otago University's Centre for AI and Public Policy in 2018. I'm also involved in commercial work in AI. I have worked on a commercial contract with the Auckland-based AI company Soul Machines since it was founded in 2016. I also work on Soul Machines' ethics policy.

How can we avoid confusion in the global conversation about AI regulation?

Discussions about AI and its oversight are happening everywhere - between friends in cafes, between colleagues in schools and workplaces, within local and national governments, in international conferences, in global citizens’ groups, in big and small tech companies. AI/tech companies participate in multiple transparency initiatives in different jurisdictions, engage with external stakeholders of many kinds, and compete aggressively with one another. The large companies also run huge lobbying operations with multiple governments, and large international PR operations. On top of all this, the technologies at the centre of these discussions are progressing at a startling pace. It’s important that the conversation about AI is broad, and stretches from high policymaking to grassroots. But how can we ensure that this broad national and international conversation is efficiently conducted, and leads to decisions in service of the common good? Lots of noise and activity is certainly not always a sign of progress. I will describe the state of the conversation as I have sampled it over the last few years, living in New Zealand, and participating in various international groups. I’ll make a couple of proposals about how discussions can be made efficient, both within individual countries and multilaterally, between countries.

Dino Pedreschi is professor of Computer Science at the University of Pisa. He has been a visiting scholar at the University of Texas at Austin (1989/90), at CWI Amsterdam (1993) and at UCLA (1995). Pedreschi’s current research interests are in data mining and logic in databases, and particularly in data analysis, in spatio-temporal data mining, and in privacy-preserving data mining. He is a member of the program committee of the main international conferences on data mining and knowledge discovery and an associate editor of the journal Knowledge and Information Systems. Professor Pedreschi served as the coordinator of the undergraduate studies in Computer Science at the University of Pisa, and as a vice-rector of the same university, with responsibility in teaching affairs. He has been granted a Google Research Award (2009) for his research on privacy-preserving data mining and anonymity-preserving data publishing. He is the co-editor of the book ”Mobility, Data Mining and Privacy”, Springer, 2008.

Future Artificial Intelligent Research

TBA

Child Neurologist and Psychiatrist at University of Pisa and IRCCS Fondazione Stella Maris. Head of INNOVATE Lab.  She has a strong expertise on elaborating new clinical and technological approaches for diagnosis and treatment of infants and children with motor developmental disorders and the personalization of exercises in tele-rehabilitation training.

Jeroen van den Hoven is University Professor at Delft University of Technology and professor of Ethics and Technology. He published widely on Ethics of Digital Technology, especially on Responsible Innovation, Value Sensitive Design, Digital Democracy, Privacy, Meaningful Human Control over Autonomous Technology and Evil Online. He is Founding Editor in Chief of Ethics and Information Technology (Springer Nature) and he is permanent member of the European Group on Ethics to the President of the European Commission. He also is a member of the High Level Advisory Group to the WHO on Ethics and AI.

Digital Ethics: Thinking about moral responsibility in the age of AI and Big Data

The Lecture will discuss a number of prominent ethical problems in a digital age and describe ways to deal with them. We will pay special attention to Responsible Innovation and Ethics by Design. The discussion will be situated against a background of regulatory and public policy approaches to the governance of big data and AI in Europe.

Professor Stewart Trost is a Professor of Paediatric Allied Health Research with conjoint appointments with The University of Queensland and Children’s Health Queensland. He is an internationally recognised research leader in device-based assessment of movement behaviours, community-based PA interventions, and client-centred therapeutic exercise programs for people with chronic and complex health conditions.

Machine learning for sensor-enabled activity recognition and habitual physical activity assessment in children and adolescents with neuro-impairment

Accelerometer-based motion sensors have become the method of choice for assessing habitual physical activity (HPA) in children and youth. However, despite their widespread use among children with typical development, calibrating accelerometer output to units of energy expenditure or physical activity intensity in children with CP presents significant methodological challenges. A number of studies have derived thresholds or cut points to broadly categorize PA intensity among children with CP. However, validation studies involving independent samples of children with CP show that cut-point approaches misclassify PA intensity 30% of the time and dramatically underestimate HPA in children with more severe motor impairments. As such, there is a critical need to develop alternative accelerometer data processing methods that provide more accurate assessments of HPA in children with CP. In this talk I will overview our work related to developing and testing machine learning PA activity classification models for children with CP.

Tommaso Turchi is an Assistant Professor in the Department of Computer Science at the University of Pisa in Italy. He holds a PhD in Human-Computer Interaction. His research focuses on Human-Centered AI and End-User Development. He has worked on various research projects related to the interaction with AI systems and is currently investigating the use of Design Fiction for AI-as-a-service applications in the medical field. His most recent work includes the development of a co-design toolkit to identify and address bias in ML-based collaborative decision-making domains.

MiniCoDe: Minimise algorithmic bias in Collaborative Decision Making with Design

In an increasingly complex everyday life, algorithms – often learnt from data, i.e. machine learning (ML) – are used to make or assist operational decisions. However, developers and researchers might not be entirely aware of how to reflect on social justice while designing ML algorithms and applications. Algorithmic social justice – i.e., designing algorithms including fairness, transparency, and accountability – aims at helping expose, counterbalance, and remedy bias and exclusion in future ML-based decision-making applications. How might we entice people to engage in more reflective practices that examine ethical consequences of ML algorithmic bias in society? I will present a Design Fiction driven methodology we developed to enable multi-disciplinary teams to perform intense, workshop-like gatherings to let emerge potential ethical issues and mitigate bias through a series of guided steps. With this group activity, we aim at helping participants to reflect on the ethical consequences of ML algorithmic bias in society. We also aim at helping them to develop a critical perspective on the use of ML algorithms in their own work.