Datasets:
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---
license: apache-2.0
task_categories:
- tabular-regression
- tabular-classification
- question-answering
language:
- en
tags:
- user modelling
- trust
size_categories:
- 10K<n<100K
configs:
- config_name: data
data_files: "data.jsonl"
---
This is a slightly edited dataset of the one [found here on GitHub](https://github.com/zouharvi/trust-intervention/).
The data contains the user interactions, their bet values, answer correctness etc.
Please contact the authors if you have any questions.
# A Diachronic Perspective on User Trust in AI under Uncertainty
> **Abstract:** In a human-AI collaboration, users build a mental model of the AI system based on its veracity and how it presents its decision, e.g. its presentation of system confidence and an explanation of the output.
> However, modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust.
> In order to build trustworthy AI, we must understand how user trust is developed and how it can be regained after potential trust-eroding events.
> We study the evolution of user trust in response to these trust-eroding events using a betting game as the users interact with the AI.
> We find that even a few incorrect instances with inaccurate confidence estimates can substantially damage user trust and performance, with very slow recovery.
> We also show that this degradation in trust can reduce the success of human-AI collaboration
> and that different types of miscalibration---unconfidently correct and confidently incorrect---have different (negative) effects on user trust.
> Our findings highlight the importance of calibration in user-facing AI application, and shed light onto what aspects help users decide whether to trust the system.
This work was presented EMNLP 2023, read it [**here**](https://aclanthology.org/2023.emnlp-main.339/).
Written by Shehzaad Dhuliawala, Vilém Zouhar, Mennatallah El-Assady, and Mrinmaya Sachan from ETH Zurich, Department of Computer Science.
```
@inproceedings{dhuliawala-etal-2023-diachronic,
title = "A Diachronic Perspective on User Trust in {AI} under Uncertainty",
author = "Dhuliawala, Shehzaad and
Zouhar, Vil{\'e}m and
El-Assady, Mennatallah and
Sachan, Mrinmaya",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.339",
doi = "10.18653/v1/2023.emnlp-main.339",
pages = "5567--5580"
}
```
<img width="400em" src="https://raw.githubusercontent.com/zouharvi/trust-intervention/main/meta/figure_1.png">
<small>
Figure 1: Diachronic view of a typical human-AI collaborative setting.
Here, at each timestep <em>t</em>, the user uses their prior mental model <em>ψ<sub>t</sub></em> to accept or reject the AI system’s answer <em>y<sub>t</sub></em>, supported by an additional message <em>m<sub>t</sub></em> comprising of the AI’s confidence, and updates their mental model of the AI system to <em>ψ<sub>t+1</sub></em>. If the message is rejected, the user invokes a fallback process to provide a different answer.
</small>
## Resources
[](https://www.youtube.com/watch?v=NrH3flpijDw)
<img width="500em" src="https://raw.githubusercontent.com/zouharvi/trust-intervention/main/meta/poster.png"> |