From spatial to spatio-temporal and, then, to mobility data. So, what’s next? It is the rise of mobility-aware integrated Big Data analytics. The Big Mobility Data Analytics (BMDA) workshop series, started in 2018 with EDBT Conference, aims at bringing together experts in the field from academia, industry and research labs to discuss the lessons they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of mobility.
Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. Therefore IDA accepts all inspiring papers for both presentation and publication. In order to create an open atmosphere that encourages discussion, the IDA symposium is intentionally small-scale and single-track.
Aula Master, Officine Garibaldi, via Gioberti 39, Pisa, Italy
Description:
In questo intervento di training e disseminazione, verrà illustrata agli studenti di PhD in Intelligenza Artificiale e Informatica di diverse istituzioni (UniPi, SNS, CNR) il funzionamento dell’infrastruttura di ricerca SoBigData RI.
Verranno illustrate le potenzialità e le opportunità messe a disposizione agli studenti dall’infrastruttura e come questi possano interagire e accrescere la loro ricerca.
Verranno inoltre illustrate le modalità per accedere a risorse di calcolo offerte dall’infrastruttura.
Interverranno:
The First Workshop on Human-centered Artificial Intelligence focuses on the study of Artificial Intelligence systems that cooperate synergistically, proactively and purposefully with humans, amplifying instead of replacing human intelligence. Human-centered AI research aims for AI systems that work together with humans, emphasizing the need for adaptive, collaborative, responsible, interactive and coevolving intelligent human-AI ecosystems.
Humans, by nature, are said to be social, enthusiastic living beings. Interacting and discussing with people is crucial to them as food, water, and shelter are for their survival. While face-to-face communication has proven to enhance the quality of a person’s life, the effects of online interactions and discussions on individuals and society are more blurred and still widely debated.
The National Ph.D. in Artificial Intelligence has a new cohort of Ph.D. students is starting his path (39th cycle).
As usual, the staff has organized a Welcome Meeting to allow the new students meet the board members and the "old" students.
The 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale) , which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.
In the past, machine learning and decision-making have been treated as independent research areas. However, with the increasing emphasis on human-centered AI, there has been a growing interest in combining these two areas. Researchers have explored approaches that aim to complement human decision-making rather than replace it, as well as strategies that leverage machine predictions to improve overall decision-making performance.
By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of machine learning and knowledge discovery approaches and have opened up new opportunities for supporting and assessing educational experiences in a data-driven fashion.
In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has an immense potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, and reducing accountability. Unfortunately, these risks arise in different applications.
The World Conference on Explainable Artificial Intelligence (XAI 2023) is an annual event that aims to bring together researchers, academics, and professionals, promoting the sharing and discussion of knowledge, new perspectives, experiences, and innovations in the field of eXplainable Artificial Intelligence (XAI).
The AI Act (AIA) is a landmark EU legislation to regulate Artificial Intelligence based on its capacity to cause harm. Like the EU’s General Data Protection Regulation (GDPR), the AIA could become a global standard, determining to what extent AI can have an effect on our lives wherever we might be. The AI Act is already making waves internationally. In late September, Brazil’s Congress passed a bill that creates a legal framework for artificial intelligence.
Ital-IA è il terzo Convegno Nazionale CINI sull'Intelligenza Artificiale, organizzato per sviluppare obiettivi comuni tra istituzioni pubbliche, industria italiana e la ricerca scientifica delle università e dei centri di ricerca nazionali. Ital-IA ha l'ambizione di "fare rete nazionale" tra tutte le azioni che si stanno disegnando in questi mesi in Italia per cogliere le potenzialità di sviluppo legate alle tecnologie dell'Intelligenza Artificiale.
09:30 - Ethical, Trustworthy, Interactive AI:
Explainability and Interactive AI
Fosca Giannotti Riccardo Guidotti Anna Monreale Salvatore Rinzivillo
Eleonora Cappuccio
Francesco Spinnato
Francesco Bodria
Mattia Setzu
Andrea Beretta
Privacy in statistical databases is about finding tradeoffs to the tension between the increasing societal and economical demand for accurate information and the legal and ethical obligation to protect the privacy of individuals and enterprise which are the respondents providing the statistical data. In the case of statistical databases, the motivation for respondent privacy is one of survival: data collectors cannot expect to collect accurate information from individual or corporate respondents unless these feel the privacy of their responses is guaranteed.
In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has an immense potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, and reducing accountability. Unfortunately, these risks arise in different applications.