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The System Model and the User Model: Exploring AI Dashboard Design

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arxiv 2305.02469 v1 pith:DJTHUHZG submitted 2023-05-04 cs.HC cs.AIcs.LG

classification cs.HCcs.AIcs.LG
keywords modelmodelssystemsystemsuserdisplayshoulddashboards
verification ladder T0 review T1 audit T2 compute T3 formal
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This is a speculative essay on interface design and artificial intelligence. Recently there has been a surge of attention to chatbots based on large language models, including widely reported unsavory interactions. We contend that part of the problem is that text is not all you need: sophisticated AI systems should have dashboards, just like all other complicated devices. Assuming the hypothesis that AI systems based on neural networks will contain interpretable models of aspects of the world around them, we discuss what data such dashboards might display. We conjecture that, for many systems, the two most important models will be of the user and of the system itself. We call these the System Model and User Model. We argue that, for usability and safety, interfaces to dialogue-based AI systems should have a parallel display based on the state of the System Model and the User Model. Finding ways to identify, interpret, and display these two models should be a core part of interface research for AI.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    VLAC-Cut-guided multi-robot HITL post-training reaches 80–95% success and 1.7–4.2× throughput over the base VLA, outperforming HITL-only under the same human budget.

  2. What Does it Mean for a Neural Network to Learn a "World Model"?

    cs.AI 2025-07 conditional novelty 6.0 of 10

    Defines a world model as a simple commutative-diagram factorization through an intermediate representation, with conditions that the model be learned and emergent rather than inherited from input or output.

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