AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making
Gilles Eerlings, Sebe Vanbrabant, Jori Liesenborgs, Gustavo Rovelo Ruiz, Davy Vanacken, Kris Luyten

TL;DR
AI-Spectra introduces a visual dashboard leveraging model multiplicity to improve transparency and decision-making in AI systems by visualizing multiple model predictions with Chernoff Bots.
Contribution
This work presents a novel visual dashboard, AI-Spectra, that effectively conveys multiple AI model predictions to enhance user understanding and trust.
Findings
Validated with MNIST dataset experiments
Facilitated quick interpretation of model consensus and divergence
Improved user understanding of model behavior
Abstract
We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models yielding equally valid outcomes or predictions for the same task, thus relying on many simultaneous "expert advisors" that can have different opinions. Dealing with multiple AI models that generate potentially divergent results for the same task is challenging for users to deal with. It helps users understand and identify AI models are not always correct and might differ, but it can also result in an information overload when being confronted with multiple results instead of one. AI-Spectra leverages model multiplicity by using a visual dashboard designed for conveying what AI models generate which results while minimizing the cognitive effort to detect consensus among models and what type of models might have different opinions. We use…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSimulation Techniques and Applications · Advanced Data Processing Techniques
