Browsing by Author "Surodina, Svitlana"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Visualizing Complex Data Decisions: Design Study for Ethical Factors in AI Clinical Decision Support Systems
    (The Eurographics Association, 2024) Surodina, Svitlana; Volkova, Daria; Abdul-Rahman, Alfie; Borgo, Rita; Hunter, David; Slingsby, Aidan
    Despite the proliferation of Artificial Intelligence (AI) technologies, their uptake in clinical settings has been lacking progress due to complexities of sociotechnical factors and intricacies of decision-making. Fairness and bias of predictive models, ethics and quality of training data, and corresponding compliance requirements become especially pressing while remaining fuzzy and implicit for various stakeholders who make the decisions. We present learnings and future directions from a design study with domain experts and propose a novel approach to encoding and collaborative reasoning on complex requirements for AI-Empowered Clinical Decision Support System (AI-CDSS) design based on Knowledge Graph (KG) representation. The insights will be useful to the community of visualization researchers who work on ethical AI-CDSS design and conduct design studies with clinical partners.
  • Thumbnail Image
    Item
    What Makes a Design Study Sustainable in Complex Domains? A Characterisation Framework for Regulated, Stakeholder-Rich Contexts
    (The Eurographics Association, 2025) Surodina, Svitlana; Borgo, Rita; Sheng, Yun; Slingsby, Aidan
    Design studies are a core methodology in visualisation for solving real-world problems, but applying them in complex domains, such as clinical visual analytics, encounters well-recognised challenges. Existing frameworks provide rigour but often fall short in guiding systematic long-term, cross-disciplinary collaborations and sustainable tool adoption in high-stakes settings. This paper introduces a two-phase framework combining extended domain-characterisation methods and grounded in the established design study methodologies to frame the industry-level precondition analysis from project-specific design. Validated through AI-Enabled Clinical Decision Support Systems (AI-CDSS) case studies, our approach standardises domain constraints upfront, accelerates project onboarding, and lays the groundwork for cross-project comparison for sustainable, scalable visualisation research.