mlsanityData to 14 Oct 2026

Bridging the Gap in Hybrid Decision-Making Systems

arXiv 2409.19415 ↗First posted 28 Sep 2024cs.AI cs.HCcited by 1

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Abstract (arXiv)

We introduce BRIDGET, a novel human-in-the-loop system for hybrid decision-making, aiding the user to label records from an un-labeled dataset, attempting to ``bridge the gap'' between the two most popular Hybrid Decision-Making paradigms: those featuring the human in a leading position, and the other with a machine making most of the decisions. BRIDGET understands when either a machine or a human user should be in charge, dynamically switching between two statuses. In the different statuses, BRIDGET still fosters the human-AI interaction, either having a machine learning model assuming skeptical stances towards the user and offering them suggestions, or towards itself and calling the user back. We believe our proposal lays the groundwork for future synergistic systems involving a human and a machine decision-makers.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 28 Sep 2024.

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8 of 16 references.

References · 8

  1. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  2. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  3. 2020Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-makingcited by 59
  4. 2021Interactive Label Cleaning with Example-based Explanationscited by 49
  5. 2020Learning in the Wild with Incremental Skeptical Gaussian Processescited by 13
  6. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  7. 2020Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairnesscited by 6
  8. 2024Learning To Guide Human Decision Makers With Vision-Language Modelscited by 4

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