On the fairness impacts of private ensembles models

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A fairness analysis on private aggregation of teacher ensembles

C Tran, MH Dinh, K Beiter, F Fioretto - arxiv preprint arxiv:2109.08630, 2021 - arxiv.org
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine
learning framework. It combines multiple learning models used as teachers for a student …
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Differentially Private Data Release on Graphs: Inefficiencies and Unfairness

F Fioretto, D Sen, J Ziani - arxiv preprint arxiv:2408.05246, 2024 - arxiv.org
Networks are crucial components of many sectors, including telecommunications,
healthcare, finance, energy, and transportation. The information carried in such networks …
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Privacy at a Price: Exploring its Dual Impact on AI Fairness

M Yang, M Ding, Y Qu, W Ni, D Smith… - arxiv preprint arxiv …, 2024 - arxiv.org
The worldwide adoption of machine learning (ML) and deep learning models, particularly in
critical sectors, such as healthcare and finance, presents substantial challenges in …
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