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"The Humane AI vision of AI for social good"
Paul Lukowicz
"The Humane AI vision of AI for social good"
Paul Lukowicz
Short bio:
Prof. Dr. Paul Lukowicz is both Scientific Director of the German Research Center for Artificial Intelligence (DFKI GmbH) in Kaiserslautern and Chair of Embedded Intelligence in the Computer Science Department at the TU of Kaiserslautern (TUK) since 2012. His main research areas are in context-specific, wearable recognition systems which include pattern recognition, system architectures, models, complex self-organising systems and their applications.
"The personal and social dimensions of human-centered AI"
Fosca Giannotti and Dino Pedreschi
The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity, and understanding. On the other side, this reveals also a social dimension of AI, as increasingly complex socio-technical systems emerge, made by interacting people and intelligent agents. The lecture will address both the individual and social dimension of such collaboration with focus i) on "Explainable AI" providing a reasoned introduction to the work and the research challenges to the work of Explainable AI for Decision Making (XAI) ; ii) on the undesired emerging network effects of social AI systems, as well as the design of transparent mechanisms for decentralized collaboration and decentralized personal data ecosystems that help toward desired aggregate outcomes .e., toward the realization of the agreed set of values and objectives at collective levels, such as accessible and sustainable mobility in cities, diversity, and pluralism in the public, fair distribution of economic resources environmental sustainability, a fair and inclusive job market.
"The personal and social dimensions of human-centered AI"
Fosca Giannotti and Dino Pedreschi
The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity, and understanding. On the other side, this reveals also a social dimension of AI, as increasingly complex socio-technical systems emerge, made by interacting people and intelligent agents. The lecture will address both the individual and social dimension of such collaboration with focus i) on "Explainable AI" providing a reasoned introduction to the work and the research challenges to the work of Explainable AI for Decision Making (XAI) ; ii) on the undesired emerging network effects of social AI systems, as well as the design of transparent mechanisms for decentralized collaboration and decentralized personal data ecosystems that help toward desired aggregate outcomes .e., toward the realization of the agreed set of values and objectives at collective levels, such as accessible and sustainable mobility in cities, diversity, and pluralism in the public, fair distribution of economic resources environmental sustainability, a fair and inclusive job market.
Short bio:
Short bio:
Fosca Giannotti is a director of research of computer science at the Information Science and Technology Institute “A. Faedo” of the National Research Council, 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 currently the coordinator of SoBigData, 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.
Short bio:
Short bio:
Dino Pedreschi is a Professor of Computer Science at the University of Pisa, and a pioneering scientist in mobility data mining, social network mining and privacy-preserving data mining. He co-leads with Fosca Giannotti the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and the Information Science and Technology Institute of the Italian National Research Council, one of the earliest research lab centered on data mining. His research focus is on big data analytics and mining and their impact on society. He is a founder of the Business Informatics MSc program at Univ. Pisa, a course targeted at the education of interdisciplinary data scientists. Dino has been a visiting scientist at Barabasi Lab (Center for Complex Network Research) of Northeastern University, Boston (2009-2010), and earlier at the University of Texas at Austin (1989-90), at CWI Amsterdam (1993) and at UCLA (1995). In 2009, Dino received a Google Research Award for his research on privacy-preserving data mining.
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