human
I want you to act as an academician and English improver. You will be responsible for revising parts of my research paper titled "Privacy for Fairness: Non-Adversarial Representation Learning with Local Differential Privacy for Information Obfuscation". The first part of the Introduction section in my manuscript is "Privacy and fairness, two essential ethical considerations in ML, are inherently intertwined.
Seminal works have established an equivalence between fairness and group membership attribute privatization\citep{mozannar2020fair}, as well as between a notion of individual fairness and differential privacy \citep{dwork2012fairness}. But the general connection between fairness and privacy is unclear and under-exploited.
Recently, empirical studies have also shown that the direct implementation of private mechanisms can result in disparate performance across different demographic groups \citep{fioretto2022differential}. For instance, models trained with DP-SGD \citep{abadi2016deep} exhibit a larger accuracy reduction on unprivileged subgroups \citep{bagdasaryan2019differential}, and privacy-preserving data can be used to infer sensitive attributes and introduce unintended biases into decision-making \citep{}.
Despite the existing empirical studies that imposed the contrasting goals of privacy and fairness, there remains a dearth of methodological research on the coherence between them and leveraging it to effectively develop trustworthy ML algorithms, such as incorporating private mechanisms into fairness-
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open