=Paper=
{{Paper
|id=Vol-3303/keynote1
|storemode=property
|title=Scientific Challenges, Practical Methodologies and Policy Perspectives for Trustworthy Artificial Intelligence
|pdfUrl=https://ceur-ws.org/Vol-3303/keynote1.pdf
|volume=Vol-3303
|authors=Emilia Gómez
|dblpUrl=https://dblp.org/rec/conf/recsys/Gomez22
}}
==Scientific Challenges, Practical Methodologies and Policy Perspectives for Trustworthy Artificial Intelligence==
<pdf width="1500px">https://ceur-ws.org/Vol-3303/keynote1.pdf</pdf>
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Scientific Challenges, Practical Methodologies and
Policy Perspectives for Trustworthy Artificial
Intelligence
Keynote

Emilia Gómez1,2
1
    Joint Research Centre - European Commission, Seville, Spain
2
    Music Technology Group - Universitat Pompeu Fabra, Barcelona, Spain


                                         Abstract
                                         Artificial intelligence (AI) systems, when applied in practical applications, have an impact on human
                                         behaviour. On the one hand, AI provides cognitive assistance to humans, such as helping us to interpret
                                         data more efficiently and discover hidden knowledge in large data resources. On the other hand, these
                                         AI systems also affect human decision making and cognitive and socio-emotional development. In this
                                         seminar I will provide an overview of the research carried out at HUMAINT (Human Behaviour and
                                         Machine Intelligence), an interdisciplinary research project carried out at the European Commission’s
                                         Joint Research Centre. The goal of the project is to study the impact of AI on human behaviour, and
                                         aims to provide evidence-based scientific support to the European policymaking process in this field.
                                         I will present our policy context, project approach and outcomes, focusing on four core applications
                                         (facial processing, automated driving, child-AI interaction and music recommendation) and connected to
                                         practical methodologies for fairness, diversity, transparency and human oversight.




Speaker biography
Emilia Gómez holds BSc and MSc degrees in Electrical Engineering and a PhD degree in Com-
puter Science. She is a principal investigator on Human and Machine Intelligence (HUMAINT)
at the Joint Research Centre (European Commission). She is also a guest professor at the Music
Technology Group, Universitat Pompeu Fabra, Barcelona. Her research is grounded in the
Music Information Retrieval field, where she has developed data-driven technologies to support
music listening experiences, being the first female president of ISMIR. Starting from the music
domain, she studies the impact of artificial intelligence on human decision making, cognitive
and socio-emotional development. Her research interests include fairness and transparency in
algorithmic systems, the impact of artificial intelligence on jobs, and the how these systems
affect children. She is currently a member of the Spanish National Council for AI and the OECD
One AI expert group.




ORSUM@ACM RecSys 2022: 5th Workshop on Online Recommender Systems and User Modeling, jointly with the 16th
ACM Conference on Recommender Systems, September 23rd, 2022, Seattle, WA, USA
                                       © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)

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