@inproceedings{b4f750c9fe0a443a8d1d8c7a3038bddb,
title = "Whose side are ethics codes on? Power, responsibility and the social good",
abstract = "The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the “social good”, of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes..",
keywords = "Data science, Digital differential vulnerability, Digital vulnerability, Ethics codes, Public interest technology, Social movements",
author = "Washington, {Anne L.} and Rachel Kuo",
note = "Publisher Copyright: {\textcopyright} 2020 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020 ; Conference date: 27-01-2020 Through 30-01-2020",
year = "2020",
month = jan,
day = "27",
doi = "10.1145/1234567890",
language = "English (US)",
series = "FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency",
publisher = "Association for Computing Machinery, Inc",
pages = "230--240",
booktitle = "FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency",
}