In AI research, model multiplicity can help users better understand the diversity of AI predictions. Our new system “AI-Spectra” provides a visual dashboard to harness this concept effectively. Instead of relying on a single AI model, AI-Spectra uses multiple models—each seen as an expert—to produce predictions for the same task. This helps users see not only what different models agree or disagree on, but also why these differences occur. Gilles Eerlings (a FAIR PhD student ) and Sebe Vanbrabant where the main contributors for this work and combined machine learning, model multiplicity and visualisations that focus on the characteristics of an AI model, instead of explaining the behaviour.

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Model Multiplicity in Interactive Software Systems

We got a workshop paper accepted, presenting the initial work of Gilles Eerlings et al. We explore how model multiplicity can be a potential answer to reduce overtrust in AI, as well as avoid undertrust. Still a lot of work that lies ahead, but this seems like a promising direction.

Citation

@inproceedings{luyteneerlings-modelmultiplicity2024,
  author = {Kris Luyten and Gilles Eerlings and Jori Liesenborgs and Gustavo {Rovelo Ruiz} and Sebe Vanbrabant and Davy Vanacken},
  title = {Opportunities and Challenges of Model Multiplicity in Interactive Software Systems},
  booktitle = {The Second Workshop on Engineering Interactive Systems Embedding AI Technologies},
  year = {2024}
}

Abstract

The proliferation of artificial intelligence (AI) in interactive systems has led to significant challenges in model integration, but also end-user-related aspects such as over- and undertrust. This paper explores how multiple AI models with the same performance and behavior but different internal workings—a phenomenon called model multiplicity—affect system integration and user interaction. We discuss the implications of model multiplicity for transparency, trust, and operational effectiveness in interactive software systems.

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