@inproceedings{maruf-etal-2024-generating-simple,
title = "Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models",
author = "Maruf, Sameen and
Zukerman, Ingrid and
Situ, Xuelin and
Paris, Cecile and
Haffari, Gholamreza",
editor = "Mahamood, Saad and
Minh, Nguyen Le and
Ippolito, Daphne",
booktitle = "Proceedings of the 17th International Natural Language Generation Conference",
month = sep,
year = "2024",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.inlg-main.9",
pages = "103--120",
abstract = "In this paper, we generate and compare three types of explanations of Machine Learning (ML) predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model{'}s predictions are applicable. The results of our user study show that (1) conservative and unifying explanations are liked equally and considered largely equivalent in terms of completeness, helpfulness for understanding the AI, and enticement to act, and both are deemed better than simple explanations; and (2)users{'} views about explanations are influenced by the (dis)agreement between the ML model{'}s predictions and users{'} estimations of these predictions, and by the inclusion/omission of features users expect to see in explanations.",
}
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<abstract>In this paper, we generate and compare three types of explanations of Machine Learning (ML) predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model’s predictions are applicable. The results of our user study show that (1) conservative and unifying explanations are liked equally and considered largely equivalent in terms of completeness, helpfulness for understanding the AI, and enticement to act, and both are deemed better than simple explanations; and (2)users’ views about explanations are influenced by the (dis)agreement between the ML model’s predictions and users’ estimations of these predictions, and by the inclusion/omission of features users expect to see in explanations.</abstract>
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%0 Conference Proceedings
%T Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models
%A Maruf, Sameen
%A Zukerman, Ingrid
%A Situ, Xuelin
%A Paris, Cecile
%A Haffari, Gholamreza
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F maruf-etal-2024-generating-simple
%X In this paper, we generate and compare three types of explanations of Machine Learning (ML) predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model’s predictions are applicable. The results of our user study show that (1) conservative and unifying explanations are liked equally and considered largely equivalent in terms of completeness, helpfulness for understanding the AI, and enticement to act, and both are deemed better than simple explanations; and (2)users’ views about explanations are influenced by the (dis)agreement between the ML model’s predictions and users’ estimations of these predictions, and by the inclusion/omission of features users expect to see in explanations.
%U https://aclanthology.org/2024.inlg-main.9
%P 103-120
Markdown (Informal)
[Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models](https://aclanthology.org/2024.inlg-main.9) (Maruf et al., INLG 2024)
ACL