Simone Stumpf

schoolGlasgow Universitybar_chart#1744 Global Rank

Total Papers

HCI Rank

#1744

Research Areas

Years Active

Recent Publications

"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment

Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness…

CHI '26CorrespondingExplainable AI (XAI) +2

Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise

Artificial intelligence (AI) applications have become ubiquitous in their impact on individuals and society, highlighting a crucial need for their responsible development. Recent research has called for participatory AI auditing, empowering individuals without AI expertise to audit AI applications throughout the entir…

CSCW '25CorrespondingFacilitating Equity and Fairness in Tech

EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders

Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the need to accommodate stakeholders' diverse fairness understandings, efforts are underway to solicit their input. However, conveying AI fairne…

CHI '252nd AuthorHonorable MentionAI Ethics, Fairness & Accountability +1

Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems

Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain kno…

CHI '242nd AuthorExplainable AI (XAI) +1

EXMOS: Explanatory Model Steering through Multifaceted Explanations and Data Configurations

Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potential data issues for model improvement remains unexplored. This researc…

CHI '223rd AuthorBest PaperUniversal & Inclusive Design +2

Investigating Daily Practices of Self-care to Inform the Design of Supportive Health Technologies for Living and Ageing Well with HIV

We report on a Diary Study investigating daily practices of Self-care by seven UK adults living with Human Immunodeficiency Virus (HIV), to understand their routines, experiences, needs and concerns, informing Self-care technology design to support living well. We advance a developing HCI literature evidencing how dig…

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