What's Privacy Good for? Measuring Privacy as a Shield from Harms due to Personal Data Use
Sri Harsha Gajavalli, Junichi Koizumi, Rakibul Hasan

TL;DR
This paper introduces a harm-centric view of privacy, focusing on how privacy can prevent various harms from personal data use, supported by a study with students assessing perceptions of different privacy-related harms.
Contribution
It operationalizes a new harm-centric privacy framework and empirically evaluates perceptions of harms across different contexts and demographics.
Findings
14 harms are internally consistent and represent a general notion of privacy harms.
Perceptions of harms vary across contexts and demographic groups.
The study provides practical guidance for improving privacy in education and employment.
Abstract
We propose a harm-centric conceptualization of privacy that asks: What harms from personal data use can privacy prevent? The motivation behind this research is limitations in existing privacy frameworks (e.g., Contextual Integrity) to capture or categorize many of the harms that arise from modern technology's use of personal data. We operationalize this conceptualization in an online study with 400 college and university students. Study participants indicated their perceptions of different harms (e.g., manipulation, discrimination, and harassment) that may arise when artificial intelligence-based algorithms infer personal data (e.g., demographics, personality traits, and cognitive disability) and use it to identify students who are likely to drop out of a course or the best job candidate. The study includes 14 harms and six types of personal data selected based on an extensive…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Ethics and Social Impacts of AI · Privacy-Preserving Technologies in Data
