DISA

When? Friday April 19th 14-16
Where? Onsite: D1173 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/RupZWWgzawV25HhH8 by April 17th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: State-of-the-art and ongoing research on the Visualization of Temporal and Multivariate Networks – Claudio Linhares
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Explainable AI through Visual Analytics – Rafael Martins
15.50 -16.00 Sum up and plan for our next seminar on May 17th

Abstracts

State-of-the-art and ongoing research on the Visualization of Temporal and Multivariate Networks – Claudio Linhares
This presentation will cover an overview of current research on visualizing temporal and multivariate networks, emphasizing the challenges and advancements in representing evolving interactions and diverse attributes. Also, it will discuss ongoing research into temporal network visualization techniques, including static and dynamic approaches, and the incorporation of multivariate attributes, such as node features, edge weights, and temporal dynamics. Furthermore, it will explore the challenges of scalability, interpretability, and interactive exploration.

Explainable AI through Visual Analytics – Rafael Martins
Artificial intelligence (AI) has revolutionized decision-making processes and enhanced efficiency in various sectors and areas of knowledge and application. However, the adoption of AI models in critical domains such as healthcare, finance, and criminal justice can be hindered by their inherent opacity. The difficulty to comprehend the rationale behind AI-driven decisions can pose significant challenges, including lack of trust, legal implications, and ethical concerns. In response, the concept of Explainable AI (XAI) has emerged as a pivotal area of research, aiming to elucidate the underlying mechanisms of AI models and enhance their interpretability. In this talk I will discuss the synergy between Explainable AI and Visual Analytics, exploring how visualizations can serve as powerful tools for elucidating AI decision-making processes. I will discuss some of my own work and other interesting state-of-the-art developments in the area, with some examples of how these techniques can be applied in practice to foster trust, transparency, and accountability, hopefully bridging the gap between AI sophistication and human understanding.