January 14, 2026 at 2 PM
Charlotte Laclau (Telecom Paris) will give a seminar to the M1 and M2 Data Science students.
Title: Algorithmic Fairness and Graphs
Abstract: Machine learning systems are increasingly used in sensitive applications such as hiring, credit scoring, recommendation, or fraud detection, raising important concerns about fairness. These systems may reproduce or amplify existing biases due to historical inequalities, biased data collection, or feedback effects.
In this talk, I will first introduce the main notions of algorithmic fairness, where these issues come from, and briefly review common mitigation strategies. I will then focus on a setting where fairness poses particularly subtle challenges: graph-structured data. From friendship recommendations to fraud detection, many decisions rely on network connections. I will show how biases can emerge from the graph structure itself in tasks such as edge prediction, and discuss how such structural biases can be identified and mitigated.
Deise Santana Maia ( 3D-SAM, CRIStAL)
Title: Watershed-based segmentation for remote sensing image analysis
Abstract: Attribute Profiles (APs) were originally defined as sequences of filtering operators on inclusion trees, i.e., the max- and min-trees, derived from an input image. In this talk, I present an extension of APs within the framework of graph-based hierarchical watersheds, in which semantic knowledge provided by labeled training pixels is exploited during different stages of the watershed-AP construction: within the construction of hierarchical watersheds and later during the filtering of the resulting hierarchy. The proposed method is illustrated through land cover classification and building extraction using optical remote sensing images.
Centrale Lille, Amphi Goubet
français
English