Browsing Machine Learning Methods in Visualisation for Big Data by Subject "CCS Concepts: Human-centered computing → Visualization; Theory of computation → Design and analysis of algorithms"
(The Eurographics Association, 2024) Hong, Seok-Hee; Liotta, Giuseppe; Montecchiani, Fabrizio; Nöllenburg, Martin; Piselli, Tommaso; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
Motivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence, stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for straight-line drawings of graphs, a foundational paradigm in the field. We empirically investigate the following research questions: (i) What is the price of incorporating fairness constraints in straight-line drawings? (ii) How unfair is a straight-line drawing that does not optimize fairness as a primary objective? To tackle these questions, we implement an algorithm based on gradient-descent that can compute straight-line drawings of graphs by optimizing multi-objective functions. We experimentally show that one can significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced readability.