Code supporting "Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News". If you use this code in your own research, please cite this paper:
@misc{abels2024mitigating,
title={Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News},
author={Axel Abels and Elias Fernandez Domingos and Ann Nowé and Tom Lenaerts},
year={2024},
eprint={2403.08829},
archivePrefix={arXiv},
primaryClass={cs.HC}
}
Our results were obtained with python3.7
analysis.ipynb contains code in support of our analysis
cdmsimulation.ipynb implements our simulated cdm setting
Requirements are given in requirements.txt and can be installed through pip install -r requirements.txt
Participant responses are given in responses.csv, whose columns match the descriptions below
| column | name description |
|---|---|
| treatment | identifier for the set of headlines presented to the participant |
| trial | trial/round in which the headline was presented |
| arm | which "arm" the headline was presented as (0=left, 1=middle, 2=right) |
| advice | the participant's response (0=very unlikely, 0.25=unlikely, 0.5=undecided, 0.75=likely, 1=very likely) |
| genuine | whether the headline was genuine (1) or altered (0) |
| headline | the headline as shown to the participant |
| original | the headline without before a possible alteration |
| expert_id | participant's identifier |
| sentiment | whether the headline reported a negative (-1) or positive (1) outcome |
| expert:ethnicity | the participant's ethnicity |
| expert:sex | the participant's sex |
| expert:age | the participant's age |
| outcome:white, outcome:black, outcome:young, outcome:old, outcome:male, outcome:female | whether the headline reported a negative (-1) or positive (1) or neutral (0) outcome for the specified group |
| trial_time | how long the participant took to respond to the trial/round |