Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
 license: apache-2.0
+task_categories:
+- tabular-regression
+- tabular-classification
+- question-answering
+language:
+- en
+tags:
+- user modelling
+- trust
+size_categories:
+- 10K<n<100K
 ---
+
+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
+
+[![Paper video presentation](https://img.youtube.com/vi/NrH3flpijDw/0.jpg)](https://www.youtube.com/watch?v=NrH3flpijDw)
+
+
+<img width="500em" src="https://raw.githubusercontent.com/zouharvi/trust-intervention/main/meta/poster.png">