The Annotation of the Complexity and Diverse Features of Decision Tasks in the Human-AI Decision-Making Literature

doi:10.4121/ba1a5ee2-4327-48eb-9e20-6e6f4f57bc9d.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/ba1a5ee2-4327-48eb-9e20-6e6f4f57bc9d
Datacite citation style:
Salimzadeh, Sara (2024): The Annotation of the Complexity and Diverse Features of Decision Tasks in the Human-AI Decision-Making Literature . Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/ba1a5ee2-4327-48eb-9e20-6e6f4f57bc9d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Delft University of Technology logo
usage stats
51
views
13
downloads
time coverage
01-01-2019 till 31-08-2022
licence
Apache-2.0

This dataset contains annotations of various dimensions of decision-making tasks explored in the human-AI collaboration literature from January 2019 to August 2022. These factors include decision complexity, decision domain, task type, and required expertise. The dataset was created to analyze the complexity of decision-making tasks in the literature and identify research gaps. It is being made public to serve as supplementary data for publications and the PhD thesis of Sara Salimzadeh, as well as to enable other researchers to utilize this data in their own work.

history
  • 2024-06-28 first online, published, posted
publisher
4TU.ResearchData
format
zipped file contains CSV and PDF
funding
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Sciences, Department of Software Technology
AI for Fintech Research Lab at ING Group

DATA

files (1)