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20202026
most citedThe State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

190 citations · 634 across the 21 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework

Angelos Chatzimparmpas, Evanthia Dimara

AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are…

cs.HC2024★ 8 cited

How to Distinguish AI-Generated Images from Authentic Photographs

Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas +2

The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between re…

cs.HC2024★ 15 cited

Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023

Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren

Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics…

cs.HC2024★ 1 cited

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca +1

Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncov…

cs.HC2024

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Dongping Zhang, Angelos Chatzimparmpas, Negar Kamali +1

As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the presentation of c…