ON THE APPLICATION AND IMPACT OF ϵ-DP AND FAIRNESS IN AMBULANCE ENGAGEMENT TIME PREDICTION

Selene Cerna; Catuscia Palamidessi

ON THE APPLICATION AND IMPACT OF ϵ-DP AND FAIRNESS IN AMBULANCE ENGAGEMENT TIME PREDICTION

Selene Cerna
, Catuscia Palamidessi

Research output: Contribution to conference › Paper › peer-review

Abstract

This study elaborates on a complete pipeline for the development of a private and fair Machine Learning (ML) model to predict ambulance engagement time. It was shown that sensitive variables reduced their impact on model building with Random Forest as the differential privacy budget (ϵ) decreased with the GRR and Geometric mechanisms. Also, the application of the Reweighing fairness mechanism negatively affected fairness in private models. Finally, it is possible to keep firefighters’ and victims’ privacy, recovering an ML model with good performance.

Original language	English
Publication status	Published - 1 Jan 2023
Event	1st Tiny Papers at 11th International Conference on Learning Representations, Tiny Papers @ ICLR 2023 - Kigali, Rwanda Duration: 5 May 2023 → 5 May 2023

Conference

Conference	1st Tiny Papers at 11th International Conference on Learning Representations, Tiny Papers @ ICLR 2023
Country/Territory	Rwanda
City	Kigali
Period	5/05/23 → 5/05/23

Cite this

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title = "ON THE APPLICATION AND IMPACT OF ϵ-DP AND FAIRNESS IN AMBULANCE ENGAGEMENT TIME PREDICTION",

abstract = "This study elaborates on a complete pipeline for the development of a private and fair Machine Learning (ML) model to predict ambulance engagement time. It was shown that sensitive variables reduced their impact on model building with Random Forest as the differential privacy budget (ϵ) decreased with the GRR and Geometric mechanisms. Also, the application of the Reweighing fairness mechanism negatively affected fairness in private models. Finally, it is possible to keep firefighters{\textquoteright} and victims{\textquoteright} privacy, recovering an ML model with good performance.",

author = "Selene Cerna and Catuscia Palamidessi",

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AU - Palamidessi, Catuscia

PY - 2023/1/1

Y1 - 2023/1/1

N2 - This study elaborates on a complete pipeline for the development of a private and fair Machine Learning (ML) model to predict ambulance engagement time. It was shown that sensitive variables reduced their impact on model building with Random Forest as the differential privacy budget (ϵ) decreased with the GRR and Geometric mechanisms. Also, the application of the Reweighing fairness mechanism negatively affected fairness in private models. Finally, it is possible to keep firefighters’ and victims’ privacy, recovering an ML model with good performance.

AB - This study elaborates on a complete pipeline for the development of a private and fair Machine Learning (ML) model to predict ambulance engagement time. It was shown that sensitive variables reduced their impact on model building with Random Forest as the differential privacy budget (ϵ) decreased with the GRR and Geometric mechanisms. Also, the application of the Reweighing fairness mechanism negatively affected fairness in private models. Finally, it is possible to keep firefighters’ and victims’ privacy, recovering an ML model with good performance.

UR - https://www.scopus.com/pages/publications/105026756493

M3 - Paper

AN - SCOPUS:105026756493

T2 - 1st Tiny Papers at 11th International Conference on Learning Representations, Tiny Papers @ ICLR 2023

Y2 - 5 May 2023 through 5 May 2023

ER -

ON THE APPLICATION AND IMPACT OF ϵ-DP AND FAIRNESS IN AMBULANCE ENGAGEMENT TIME PREDICTION

Abstract

Conference

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