We are excited to announce an upcoming seminar featuring Tiago Araújo, titled “Unveiling the black box: Explainability on Machine Learning Techniques.” The seminar will delve into the importance of explainability in machine learning and its implications for building trust and ensuring fairness in predictive models.
Machine learning models are often regarded as “black boxes” due to their lack of interpretability, despite their impressive accuracy in various domains. Understanding and justifying the behavior of these models is crucial for users and stakeholders to trust their decisions. In this seminar, Tiago Araújo will explore the field of explainable machine learning, which aims to make machine learning models more transparent and interpretable for human understanding.
During the talk, Tiago will provide an overview of various techniques and challenges in the realm of explainable machine learning. He will discuss methods and approaches that can shed light on the decision-making process of machine learning models, making them more interpretable and accountable. By unraveling the “black box,” we can gain valuable insights into how these models work and address potential biases or issues that may arise.
Tiago Araújo is a post-doctoral fellow at IEETA – University of Aveiro. His research focuses on applying computational techniques to leverage human perception features, particularly in the field of vision, to advance knowledge in various domains. His areas of expertise encompass Information Visualization, Data Analytics, Computer Vision, Machine Learning, Explainable AI, Virtual and Augmented Reality, and Human-Computer Interaction.
The seminar will take place at the IEETA auditorium on May 17th, starting at 4 pm. Join us for this enlightening session as we unravel the mysteries of machine learning models and delve into the fascinating world of explainability.
