In fulfilment of the European Engineering Learning Innovation and Science Alliance (EELISA) Board Declaration, this school aims to align technical excellence with social impact in the upcoming generation of AI researchers and developers, by combining their tekhnè with ethos.
School programme
The school program will unfold over a week of thematic days around key chapters of the Ethics Guidelines for Trustworthy AI, drafted by the High-Level Expert Group on AI. Each day will address a specific question, thanks to lectures in the morning and practical activities in the afternoon. The five topics will be:
1) the socioeconomic impact of AI
2) EU regulations for a fair and private AI
3) explainable AI (XAI)
4) AI decision-making biases identification and AI trustworthiness assessment
5) legal liability in AI
You can find more details about the draft programme here.
Subscribe
You can attend the school either online or on-site.
Online: fill out the form
You will be able to watch the school activities through a link that you will receive a few days before the school. Registration is free, with no selection process. During the school, your questions will always be welcomed, but remote attendance cannot guarantee that you will be able to ask them, as on-site questions will be answered first.
Active participation on-site: upload your application material by February 2nd, 2023, 11:59 pm (CET)
Seats are limited to 30 participants in total. The school targets early-career researcher (e.g. research assistants, PhD students, post-docs, exceptional master students) in AI or in computational sciences. If you are not selected, you can always attend the school online.
Acknowledgements
This event is organised and funded by Paris Sciences et Lettres, Scuola Normale Superiore and Scuola Superiore Sant'Anna through the EELISA framework.
The event has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme. Grant agreement № 835294.'
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- Tags
- deep learning transformers socio-economic drivers data privacy AI Act AI explainability Algorithmicbias liability ai ethics