# Bridging the Gap in Hybrid Decision-Making Systems

arXiv: https://arxiv.org/abs/2409.19415 · HTML: https://mlsanity.com/paper/2409.19415/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 8 of 8)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Learning in the Wild with Incremental Skeptical Gaussian Processes](https://mlsanity.com/paper/2011.00928/) (2020)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness](https://mlsanity.com/paper/2011.08398/) (2020)
- [Learning To Guide Human Decision Makers With Vision-Language Models](https://mlsanity.com/paper/2403.16501/) (2024)

## Built on by (most cited first, 1 of 1)

- [Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support](https://mlsanity.com/paper/2610.12666/) (2026)

## 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.