Keynotes and Panels

Keynote speeches introduce the main topic and clear the ground for further discussion. Selected expert panelists discuss together the related issues and afterwards answer questions from the audience during the panel discussions.

After the official opening and welcome notes, during the two-day womENcourage 2020 conference you will join our distinguished speakers to learn about The Big Promise of Big Data Analytics and to explore The Artificial Intelligence: from Algorithms to Ethics.

womENcourage 2020 Opening

Welcome Note from the ADA University Patron

Welcome Note from the ACM President

Welcome Note from the ACM-W Europe Chair

The Big Promise of Big Data Analytics

After presenting their topics the keynote speakers will get together in a live panel discussion to provide a glimpse of the wide spectrum of Big Data research and applications, as well as to answer questions from the audience.

▼ Data Science to Fight the COVID-19 Pandemic: the Valencia Case

Since March 2020, Dr. Nuria Oliver has been appointed Commissioner for the President of the Valencian Region on Artificial Intelligence Strategy and Data Science applied to the fight against COVID-19. She is leading a team of over 20 data scientists, who work on large-scale modeling to answer public policy questions. As part of this work, they have launched covid19impactsurvey, one of the largest anonymous citizens’ surveys to date related to COVID-19. This talk will describe the initial results of the large-scale human mobility modeling and computational epidemiological modeling.

▼ Health Diagnostics through Audio Signals Collection and Analysis

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as diagnostic or progression indicators for diseases and disease onset. However, until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g. from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. In this talk Dr. Cecilia Mascolo will describe the initial progress in using sounds for diagnostics and the challenges this poses. She will also describe their effort to collect and analyze data over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19.

▼ Democratizing Data Access through Intelligent Data Exploration Tools

Many different data sets, generated by users, systems and sensors, are continuously being collected. These data sets contain information about scientific experiments, health, energy, education etc., and they are highly heterogeneous in nature, ranging from highly structured data in tabular form to unstructured text, images or videos. These data can potentially benefit many types of users, from analysts exploring data sets for insight, scientists looking for patterns, to dashboard interactors and consumers looking for information. While the benefit of data exploration becomes increasingly more prominent, the limitations of existing tools make accessing and combining data from different sources a non-trivial, time-consuming, and often fruitless endeavor. In this talk Dr. Georgia Koutrika will discuss about what it takes to bridge the gap between users and data, and the new generation of intelligent data exploration tools.

Moderator

The Artificial Intelligence: from Algorithms to Ethics

Following the presentations a live panel discussion will bring together the keynote speakers to provide an overview of the different aspects of the Artificial Intelligence (AI) research and applications, as well as to answer questions from the audience.

▼ Teaching proficient human interaction to intelligent computer agents

Automated agents that interact proficiently with people can be useful in supporting or replacing people in complex tasks. The inclusion of people presents novel problems for the design of automated agents’ strategies. People do not adhere to the optimal, monolithic strategies that can be derived analytically. Their behavior is affected by a multitude of social and psychological factors. In this talk, Dr. Sarit Kraus will show how combining machine learning techniques for human modeling, human behavioral models, formal decision-making and game theory approaches enables agents to interact well with people. Applications include intelligent agents that help reducing car accidents, triage agent that schedules patients to caregivers in emergency departments, agents that are used for training law-enforcement personnel, and agents that support a human operator in managing a team of low-cost robots in search and rescue tasks.

▼ Gender Fairness of Machine Learning Techniques

Gendering ICT does not only imply changing the numbers of women in Computer Science but also addressing the problem of including the gender dimension in its content to develop gendered innovations. Many studies about the fairness of ML algorithms show that they are not gender neutral due to their nature of being bottom-up data driven. For this reason the most common biases diffused in society about gender (and ethnicity) can be captured, subsumed and reinforced by them, as many ML tool applications prove. In this talk Dr. Silvana Badaloni will discuss how learning algorithms can lead to a strong reinforcement of existing social and gender bias. The problem arises mainly since little attention is paid to how data are collected, processed and organized. Transparency and explainability are two central keywords for developing a trustworthy AI.

Other Speakers

Moderator

▼ Who Makes Wiser Decisions? Men, Women or Machines? (Cancelled)

Despite the great opportunities that AI systems offer, the underlying machine learning models are often not resilient enough to avoid manipulation and sometimes do not behave as intended. In more detail, this means that there are specific types of risks for AI systems such as model biases, adversarial attacks and new ways to exploit privacy leakages. To prevent AI models from these risks and to provide safe and fair Al, the Intelligence team at T-Labs led by Claudia Pohlink together with cybersecurity researchers from Ben-Gurion University in Israel and the Berlin-based start-up Neurocat are currently working on an end-to-end ethical and technical assessment of the robustness of AI applications. The envisioned platform will enable defense mechanisms to enrich existing solutions with necessary countermeasures. As a result, this flagship project aims to pave the way for a reliable application of future AI systems.

Keynotes and Panels

womENcourage 2020 Opening