Bridging the Gap in Hybrid Decision-Making Systems
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, a...
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| creator | Mazzoni, Federico Pellungrini, Roberto Guidotti, Riccardo |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2409.19415 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2409.19415</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction |
| title | Bridging the Gap in Hybrid Decision-Making Systems |
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