Conditional Expectation Network for SHAP

Richman, Ronald; Wüthrich, Mario V.

doi:10.2139/ssrn.4514891

SSRN Journal

2023

DOI: 10.2139/ssrn.4514891

|View full text |Cite

Conditional Expectation Network for SHAP

Ronald Richman¹,

Mario V. Wüthrich²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 28 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

A multi-task network approach for calculating discrimination-free insurance prices

Lindholm,

Richman,

Tsanakas

et al. 2023

Eur. Actuar. J.

View full text Add to dashboard Cite

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models and are thus having an undesirable (and possibly illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such an approach requires full knowledge of policyholders’ protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics and produces prices that are free from proxy discrimination. We demonstrate the proposed method on both synthetic data and a real-world motor claims dataset, in which proxy discrimination can be observed. In both examples we find that the predictive accuracy of the multi-task network is comparable to a conventional feed-forward neural network, when the protected information is available for at least half of the insurance policies. However, the multi-task network has superior performance in the case when the protected information is known for less than half of the insurance policyholders.

show abstract

A multi-task network approach for calculating discrimination-free insurance prices

Lindholm,

Richman,

Tsanakas

et al. 2023

Eur. Actuar. J.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Conditional Expectation Network for SHAP

Cited by 1 publication

References 28 publications

A multi-task network approach for calculating discrimination-free insurance prices

A multi-task network approach for calculating discrimination-free insurance prices

Contact Info

Product

Resources

About