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HUMAINT Winter School: A multidisciplinary workshop on AI and its ethical, legal, social and economic impact

HUMAINT Winter School: A multidisciplinary workshop on AI and its ethical, legal, social and economic impact

(Read the full text at the web of the HUMAINT community in the JRC Science Hub. The HUMAINT project is led by Emilia Gómez)

AI and ELSE

Between February 4th and 8th Joint Research Centre hosted the HUMAINT Winter school on AI and its ethical, legal, social and economic (ELSE) impact. We had the chance to meet and discuss with more than 100 attendees from different countries and backgrounds: law and ethics, computer science, economics, robotics, social sciences. Policy makers, researchers, journalists, entrepreneurs had a chance to debate during the five days the latest advancements in AI and the impact on humans.

My winterschool experience: people coming from all over the globe,  interdisciplinary and very talented researchers who are kick-starting the interest and exploration of ethical, legal and societal issues in AI. The most substantial […] takeaway from the HUMAINT school was […] that AI is increasingly permeating every aspect of our society […]. The principal pressure should be to design and regulate AI to be accountable, fair and transparent by means of echnical standards, ethical principles and professional codes of conduct. –Fernando Martínez Plumed

The program can be found online while shortly we will make available the videos for the presentations.

The first two days comprised talks which were meant to introduce the audience to several research topics in AI's ELSE impact. The winter school started on February 4th with a welcome note by Emilia Gómez, Virginia Dignum.  Afterwards, the head of the JRC Digital Economy Unit, Alessandro Annoni presented key points of the AI EU strategy. The participants were introduced to the general field of AI, its passed development and current state by José H. Orallo.  The speaker was not short in pointing out the field's current challenges from misaligned goals between research, industry and politics to  the lack of meaningful evaluation criteria for AI advancements.

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