Summary of Ai, Meet Human: Learning Paradigms For Hybrid Decision Making Systems, by Clara Punzi et al.
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
by Clara Punzi, Roberto Pellungrini, Mattia Setzu, Fosca Giannotti, Dino Pedreschi
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive framework for understanding human-machine interactions in machine learning-based systems. The authors propose a taxonomy of Hybrid Decision Making Systems (HDMS), which aims to categorize existing techniques in computer science literature that account for human interaction with machine learning models. The HDMS framework is designed to provide both conceptual and technical insights into how humans and machines interact, making it a valuable resource for researchers and practitioners working on machine learning-based applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are increasingly being used to automate important tasks and make decisions. This has led to humans interacting with these systems more frequently. However, the way humans interact with machine learning models is not well understood. The authors of this paper aim to change that by proposing a framework for understanding how humans and machines work together. |
Keywords
* Artificial intelligence * Machine learning
