Addressing Privacy Threats from Machine Learning
It tackles privacy threats from surveillance technologies, which is an incremental call for interdisciplinary action.
The paper addresses the growing concern about machine learning applications for surveillance by providing an overview of resistance strategies and advocating for collaboration between machine learning and human-computer interaction researchers to mitigate these threats.
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.