Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk

Celia Wafa Ayad, Thomas Bonnier, Benjamin Bosch, Jesse Read, Sonali Parbhoo
Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR 219:31-49, 2023.

Abstract

Cervical cancer is a life-threatening disease and one of the most prevalent types of cancer affecting women worldwide. Being able to adequately identify and assess factors that elevate risk of cervical cancer is crucial for early detection and treatment. Advances in machine learning have produced new methods for predicting cervical cancer risk, however their complex black-box behaviour remains a key barrier to their adoption in clinical practice. Recently, there has been substantial rise in the development of local explainability techniques aimed at breaking down a model’s predictions for particular instances in terms of, for example, meaningful concepts, important features, decision tree or rule-based logic, among others. While these techniques can help users better understand key factors driving a model’s decisions in some situations, they may not always be consistent or provide faithful predictions, particularly in applications with heterogeneous outcomes. In this paper, we present a critical analysis of several existing local interpretability methods for explaining risk factors associated with cervical cancer. Our goal is to help clinicians who use AI to better understand which types of explanations to use in particular contexts. We present a framework for studying the quality of different explanations for cervical cancer risk and contextualise how different explanations might be appropriate for different patient scenarios through an empirical analysis. Finally, we provide practical advice for practitioners as to how to use different types of explanations for assessing and determining key factors driving cervical cancer risk.

Cite this Paper

BibTeX
@InProceedings{pmlr-v219-ayad23a, title = {Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk}, author = {Ayad, Celia Wafa and Bonnier, Thomas and Bosch, Benjamin and Read, Jesse and Parbhoo, Sonali}, booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference}, pages = {31--49}, year = {2023}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo and Yeung, Serene}, volume = {219}, series = {Proceedings of Machine Learning Research}, month = {11--12 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v219/ayad23a/ayad23a.pdf}, url = {https://proceedings.mlr.press/v219/ayad23a.html}, abstract = {Cervical cancer is a life-threatening disease and one of the most prevalent types of cancer affecting women worldwide. Being able to adequately identify and assess factors that elevate risk of cervical cancer is crucial for early detection and treatment. Advances in machine learning have produced new methods for predicting cervical cancer risk, however their complex black-box behaviour remains a key barrier to their adoption in clinical practice. Recently, there has been substantial rise in the development of local explainability techniques aimed at breaking down a model’s predictions for particular instances in terms of, for example, meaningful concepts, important features, decision tree or rule-based logic, among others. While these techniques can help users better understand key factors driving a model’s decisions in some situations, they may not always be consistent or provide faithful predictions, particularly in applications with heterogeneous outcomes. In this paper, we present a critical analysis of several existing local interpretability methods for explaining risk factors associated with cervical cancer. Our goal is to help clinicians who use AI to better understand which types of explanations to use in particular contexts. We present a framework for studying the quality of different explanations for cervical cancer risk and contextualise how different explanations might be appropriate for different patient scenarios through an empirical analysis. Finally, we provide practical advice for practitioners as to how to use different types of explanations for assessing and determining key factors driving cervical cancer risk.} }
Endnote
%0 Conference Paper %T Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk %A Celia Wafa Ayad %A Thomas Bonnier %A Benjamin Bosch %A Jesse Read %A Sonali Parbhoo %B Proceedings of the 8th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2023 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %E Serene Yeung %F pmlr-v219-ayad23a %I PMLR %P 31--49 %U https://proceedings.mlr.press/v219/ayad23a.html %V 219 %X Cervical cancer is a life-threatening disease and one of the most prevalent types of cancer affecting women worldwide. Being able to adequately identify and assess factors that elevate risk of cervical cancer is crucial for early detection and treatment. Advances in machine learning have produced new methods for predicting cervical cancer risk, however their complex black-box behaviour remains a key barrier to their adoption in clinical practice. Recently, there has been substantial rise in the development of local explainability techniques aimed at breaking down a model’s predictions for particular instances in terms of, for example, meaningful concepts, important features, decision tree or rule-based logic, among others. While these techniques can help users better understand key factors driving a model’s decisions in some situations, they may not always be consistent or provide faithful predictions, particularly in applications with heterogeneous outcomes. In this paper, we present a critical analysis of several existing local interpretability methods for explaining risk factors associated with cervical cancer. Our goal is to help clinicians who use AI to better understand which types of explanations to use in particular contexts. We present a framework for studying the quality of different explanations for cervical cancer risk and contextualise how different explanations might be appropriate for different patient scenarios through an empirical analysis. Finally, we provide practical advice for practitioners as to how to use different types of explanations for assessing and determining key factors driving cervical cancer risk.
APA
Ayad, C.W., Bonnier, T., Bosch, B., Read, J. & Parbhoo, S.. (2023). Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk. Proceedings of the 8th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 219:31-49 Available from https://proceedings.mlr.press/v219/ayad23a.html.

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