Addressing Privacy Threats from Machine Learning

TL;DR

This paper discusses privacy threats from machine learning applications, especially surveillance, and advocates for interdisciplinary collaboration to develop strategies for resisting such threats.

Contribution

It provides an overview of privacy-preserving strategies and emphasizes the need for collaboration between machine learning and human-computer interaction researchers.

Findings

Highlighting privacy concerns in ML applications

Proposing interdisciplinary approaches to mitigate surveillance risks

Calling for increased collaboration between ML and HCI communities

Abstract

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.