Keynote speakers

Human-Centered AI: Ensuring Human Control While Increasing Automation

by Ben Shneiderman

ABOUT THE SPEAKER

BEN SHNEIDERMAN (http://www.cs.umd.edu/~ben) is an Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://hcil.umd.edu), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland.  He is a Fellow of the AAAS, ACM, IEEE, NAI, and the Visualization Academy and a Member of the U.S. National Academy of Engineering. He has received six honorary doctorates in recognition of his pioneering contributions to human-computer interaction and information visualization. His widely-used contributions include the clickable highlighted web links, high-precision touchscreen keyboards for mobile devices, and tagging for photos.  Shneiderman’s information visualization innovations include dynamic query sliders for Spotfire, the development of treemaps for viewing hierarchical data, novel network visualizations for NodeXL, and event sequence analysis for electronic health records.
Ben is the lead author of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016).  He co-authored Readings in Information Visualization: Using Vision to Think (1999) and Analyzing Social Media Networks with NodeXL (2nd edition, 2019).  His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, 2016) describes how research can produce higher impacts. His new book on Human-Centered AI, was published by Oxford University Press in February 2022.

Human-Centered AI: Ensuring Human Control While Increasing Automation

Abstract: A new synthesis is emerging that integrates AI technologies with Human-Computer Interaction to produce Human-Centered AI (HCAI). Advocates of this new synthesis seek to amplify, augment, and enhance human abilities, so as to empower people, build their self-efficacy, support creativity, recognize responsibility, and promote social connections.

Researchers, developers, business leaders, policy makers and others are expanding the technology-centered scope of Artificial Intelligence (AI) to include Human-Centered AI (HCAI) ways of thinking. This expansion from an algorithm-focused view to embrace a human-centered perspective, can shape the future of technology so as to better serve human needs. Educators, designers, software engineers, product managers, evaluators, and government agency staffers
can build on AI-driven technologies to design products and services that make life better for the users. These human-centered products and services will enable people to better care for each other, build sustainable communities, and restore the environment. The passionate advocates of HCAI are devoted to furthering human values, rights, justice, and dignity, by building reliable, safe, and trustworthy systems.

The talk will include examples, references to further work, and discussion time for questions. These ideas are drawn from Ben Shneiderman’s new book Human-Centered AI (Oxford University Press, February 2022). Further information at: https://hcil.umd.edu/human-centered-ai


Human Behavioral Data to Help Fight COVID-19

by Nuria Oliver

ABOUT THE SPEAKER

Nuria Oliver is Cofounder and Vicepresident of ELLIS (The European Laboratory for Learning and Intelligent Systems), co-founder and Director of the Institute of Human(ity)-centric AI (ELLIS Unit Alicante), Chief Data Scientist at Data-Pop Alliance and Chief Scientific Advisor to the Vodafone Institute. She earned her Ph.D. from MIT. She is a Fellow of the ACM, IEEE and EurAI at the same time. She is an elected member of the Royal Academy of Engineering and the only Spanish scientist at SIGCHI Academy.
She has over 25 years of research experience in human-centric AI and is the author of over 180 widely cited scientific articles as well as an inventor of 40+ patents and a public speaker. She has authored the book “Artificial Intelligence, naturally”, in collaboration with the Spanish Ministry of Economy and Digital Society. Her work is regularly featured in the media and has received numerous recognitions, including the Spanish National Computer Science Award, the MIT TR100 (today TR35), the Young Innovator Award (first Spanish scientist to receive this award); the Digital European Woman of the Year Award; the Spanish Telecommunications Engineer of the Year award; the 2021 King Jaume I award in New Technologies and the 2021 Abie Technology Leadership Award.
In March of 2020, she was appointed Commissioner to the President of the Valencian Government on AI Strategy and Data Science against COVID-19. In that role, she has recently co-led ValenciaIA4COVID, the winning team of the 500k XPRIZE Pandemic Response Challenge. Their work was featured in WIRED, Politico, and MSNBC, among other media.

Human Behavioral Data to Help Fight COVID-19

Abstract: show lessThe spread of infectious diseases that are transmitted from human to human largely depends on human behavior. Hence, modeling, understanding, and predicting human behavior during a pandemic is of paramount importance. In my talk, I will describe the work that I did between March 2020 and March 2022, leading a multi-disciplinary team of 20+ volunteer scientists working very closely with the Presidency of the Valencian Government in Spain on 4 large areas at the intersection of human behavior, data, and pandemics:
(1) large-scale human mobility modeling;
(2) computational epidemiological models (both metapopulation, individual and LSTM-based models);
(3) predictive models; and
(4) a large-scale, online citizen survey called the COVID19impactsurvey (https://covid19impactsurvey.org) with over 720,000 answers worldwide. This survey has enabled us to shed light on the impact that the pandemic is having on people’s lives [3,4,5].

I will present the results obtained in each of these four areas, including winning the 500K XPRIZE Pandemic Response Challenge [1] and obtaining the best paper award at ECML-PKDD 2021 [2]. I will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey), the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level).

For those interested in knowing more, WIRED magazine published an extensive article describing our story: https://www.wired.co.uk/article/valencia-ai-covid-data.

ACKNOWLEDGMENTS

My talk describes the work of over 20 scientists from the Data Science against COVID-19 task force, including researchers from the University of Alicante, the University Miguel Hernández, the Polytechnic University of Valencia, the University Jaume I, the CEU Cardenal Herrera University and FISABIO. The work has been partially funded by grants FONDOS SUPERA COVID-19 Santander-CRUE, 2020-2021, Fundación BBVA to scientific research teams SARS-CoV-2 COVID-19, IA4COVID19 2020-2022 and the Valencian Government via their Science, Innovation and Digital Society Ministry.

REFERENCES

[1] 500K XPRIZE Pandemic Response Challenge, https://www.xprize.org/challenge/pandemicresponse/winners-results

[2] Miguel Angel Lozano, Eloy Piñol, Miguel Rebollo, Kristina Polotskaya, Miguel Angel Garcia-March, J Alberto Conejero, Francisco Escolano, Nuria Oliver, Open Data Science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge, Proc. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp 384–399, 2021 [Best paper award in Data Science]


Recommender Systems for Better Human Choices

by Francesco Ricci

ABOUT THE SPEAKER

Francesco Ricci is a full Professor at the Faculty of Computer Science, Free University of Bozen–Bolzano. He has there established a reference point for the research on recommender systems. He has been active in this community as President of the Steering Committee of the ACM Conference on Recommender Systems, from 2007 to 2010. He was previously a Senior Researcher and the Technical Director of the E-commerce and Tourism Research Lab (eCTRL), ITC-IRST, Trento, Italy, from 2000 to 2006. From 1998 to 2000, he was a System Architect with the Research and Technology Department (process and reuse technologies), Sodalia S.p.A. His research interests include recommender systems, user modeling, machine learning and ICT applications to travel and tourism. He is the author of more than two-hundreds refereed publications. According to Google Scholar, he has an H-index of 58 and around 23,000 citations. He is the Co-Editor of the Recommender Systems Handbook (Springer 2022).

Recommender Systems for Better Human Choices

Abstract: Recommender systems have been introduced as autonomous, and perhaps “dangerous”, algorithms that can predict their users’ preferences and generate recommendations for items that are actually useful for them. Therefore, a large part of the academic and industrial research has initially focussed on the problem of predicting the “true” users’ preferences. This approach has been surely influenced by the long tradition of research on user modelling and adaptation. However, in the last 10 years we have understood that giving to the users, by means of an RS, what they really want, is a simplistic reduction of the recommendation task. Hence, new goals and roles of RSs have been considered and among them, we can note those deriving from the multistakeholder and multi-criteria nature of most of the recommendation scenarios. As a consequence of this analysis, the problem of generating useful recommendations was understood as not even simple to define, and clearly much more difficult to solve. In this talk, I will reflect on some of the foundational problems of the research on RSs and indicate some promising lines of exploration that I have tried to address with my research group.