INTERNATIONAL CONFERENCE ON

AI for People:

Towards Sustainable AI

20-24 November 2021, Online


New Paper Submission Deadline:
17 October 2021 (extended)


Program

The conference aims to be accessible for anyone interested in artificial intelligence for the societal good. For this reason, some sessions will be more introductory and other more advanced.

The program is still being updated. Below you can read about the sessions that are already confirmed:

Keynote Speakers

Workshops

Workshop on AI Ethics

[more info soon]

Workshop Leader:
Jessica Morley

Jessica is policy lead in the Oxford Bennett Institute for Applied Data Science, directed by Dr. Ben Goldacre, and a graduate researcher at the Oxford Internet Institute supervised by Professor Luciano Floridi. Her research focuses on how to turn ethics principles into practices and the how to make better, safer, and more ethically justifiable, use of big data analytics in healthcare.

Workshop on Artificial Neural Networks (introductory)

Gentle introduction on Artificial Neural Networks (ANN). We will start from a Biological Neural Network (BNN) and build our way towards ANNs and the purpose of ANNs will be described in a non-mathematical way.

At the end of this session you will know:

  1. What an ANN is.
  2. What the objective of an ANN is.
  3. What it means when we ‘train’ an ANN

Workshop Leader:
Mehran Bazargani

Mehran is a post-doctoral researcher at the school of Computer Science, Insight SFI Research Centre for Data Analytics at University College Dublin (UCD), and he is the founder of ML-Dawn. His research interests fall in the realm of machine learning, specifically, anomaly detection, active learning and interpretability. You can find his publications on Google Scholar.

Workshop on AI Governance

[more info soon]

Workshop Leader:
Nicolas Moës

Nicolas is an economist by training focused on the impact of Artificial Intelligence on geopolitics, the economy and industry. He is the Brussels-based representative of The Future Society, where he has monitored European developments in the legislative framework surrounding AI.

Workshop on Neural Networks for Tumor Detection

We will go through some code in Python and Pytorch, where the objective is to create an Artificial Neural Network (ANN) for the task of tumor detection in brain MRI images.

At the end of this session you will know:

  1. What the general approach to coding an ANN is in Pytorch. (e.g., dataset classes, dataloaders, torch models,…)
  2. In practice, how training the ANN will improve its performance, and how we can measure this improvement (e.g., accuracy, confusion matrix)
  3. What it means when a model ‘overfits’, in practice and what we can learn from it

Workshop Leader:
Mehran Bazargani

Mehran is a post-doctoral researcher at the school of Computer Science, Insight SFI Research Centre for Data Analytics at University College Dublin (UCD), and he is the founder of ML-Dawn. His research interests fall in the realm of machine learning, specifically, anomaly detection, active learning and interpretability. You can find his publications on Google Scholar.

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