skip to main content
research-article
Open access

COVID-19 Brings Data Equity Challenges to the Fore

Published: 08 March 2021 Publication History

Abstract

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: They can inform local, state, and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

References

[1]
Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich, and Gautam Das. 2019a. Designing fair ranking schemes. In Proceedings of the International Conference on Management of Data (SIGMOD’19), Peter A. Boncz, Stefan Manegold, Anastasia Ailamaki, Amol Deshpande, and Tim Kraska (Eds.). ACM, 1259--1276.
[2]
Abolfazl Asudeh, Zhongjun Jin, and H. V. Jagadish. 2019b. Assessing and remedying coverage for a given dataset. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE’19). IEEE, 554--565.
[3]
Ruha Benjamin. 2019. Race after technology: Abolitionist tools for the New Jim Code. Soc. Forces 98, 4 (12 2019), 1--3.
[4]
Merlin Chowkwanyun and Adolph L. Reed Jr. 2020. Racial health disparities and Covid-19—Caution and context. New Eng. J. Med. 383 (16 July 2020), 201--203.
[5]
Maxim Grechkin, Hoifung Poon, and Bill Howe. 2018. EZLearn: Exploiting organic supervision in automated data annotation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). International Joint Conferences on Artificial Intelligence Organization, 4085--4091.
[6]
Anna Lauren Hoffmann. 2020. Terms of inclusion: Data, discourse, violence. New Media Soc.
[7]
Falaah Arif Khan and Julia Stoyanovich. 2020. Mirror, mirror. Data, Respons. Comic Series 1 (2020). Retrieved from https://dataresponsibly.github.io/comics/.
[8]
Yin Lin, Yifan Guan, Abolfazl Asudeh, and H. V. Jagadish. 2020. Identifying insufficient data coverage in databases with multiple relations. Proc. VLDB Endow. 13, 11 (2020), 2229--2242. Retrieved from http://www.vldb.org/pvldb/vol13/p2229-lin.pdf.
[9]
Luke Rodriguez, Babak Salimi, Haoyue Ping, Julia Stoyanovich, and Bill Howe. 2018. MobilityMirror: Bias-adjusted transportation datasets. In Proceedings of the 1st Workshop on Big Social Data and Urban Computing (BiDU@VLDB’18) (Communications in Computer and Information Science), Jonice Oliveira, Claudio M. de Farias, Esther Pacitti, and Giancarlo Fortino (Eds.), Vol. 926. Springer, 18--39.
[10]
Babak Salimi, Luke Rodriguez, Bill Howe, and Dan Suciu. 2019. Interventional fairness: Causal database repair for algorithmic fairness. In Proceedings of the International Conference on Management of Data (SIGMOD’19), Peter A. Boncz, Stefan Manegold, Anastasia Ailamaki, Amol Deshpande, and Tim Kraska (Eds.). ACM, 793--810.
[11]
Sebastian Schelter, Yuxuan He, Jatin Khilnani, and Julia Stoyanovich. 2020. FairPrep: Promoting data to a first-class citizen in studies on fairness-enhancing interventions. In Proceedings of the 23rd International Conference on Extending Database Technology (EDBT’20), Angela Bonifati, Yongluan Zhou, Marcos Antonio Vaz Salles, Alexander Böhm, Dan Olteanu, George H. L. Fletcher, Arijit Khan, and Bin Yang (Eds.). OpenProceedings.org, 395--398.
[12]
Dean Spade. 2015. Normal Life: Administrative Violence, Critical Trans Politics, and the Limits of Law. Duke University Press. Retrieved from http://www.jstor.org/stable/j.ctv123x7qx.
[13]
Julia Stoyanovich and Bill Howe. 2019. Nutritional labels for data and models. IEEE Data Eng. Bull. 42, 3 (2019), 13--23. Retrieved from http://sites.computer.org/debull/A19sept/p13.pdf.
[14]
Julia Stoyanovich, Bill Howe, and H. V. Jagadish. 2020. Responsible data management. PVLDB 13, 12 (2020), 3474--3489.
[15]
Julia Stoyanovich and Armanda Lewis. 2019. Teaching responsible data science: Charting new pedagogical territory. CoRR abs/1912.10564 (2019).
[16]
Julia Stoyanovich, Ke Yang, and H. V. Jagadish. 2018. Online set selection with fairness and diversity constraints. In Proceedings of the 21st International Conference on Extending Database Technology (EDBT’18), Michael H. Böhlen, Reinhard Pichler, Norman May, Erhard Rahm, Shan-Hung Wu, and Katja Hose (Eds.). OpenProceedings.org, 241--252.
[17]
Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, and Julia Stoyanovich. 2019. MithraLabel: Flexible dataset nutritional labels for responsible data science. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM’19), Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 2893--2896.
[18]
Ke Yang, Vasilis Gkatzelis, and Julia Stoyanovich. 2019. Balanced ranking with diversity constraints. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19), Sarit Kraus (Ed.). ijcai.org, 6035--6042.
[19]
Ke Yang, Joshua R. Loftus, and Julia Stoyanovich. 2020. Causal intersectionality for fair ranking. CoRR abs/2006.08688 (2020).
[20]
Ke Yang and Julia Stoyanovich. 2017. Measuring fairness in ranked outputs. In Proceedings of the 29th International Conference on Scientific and Statistical Database Management. ACM, 22:1--22:6.
[21]
Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, H. V. Jagadish, and Gerome Miklau. 2018. A nutritional label for rankings. In Proceedings of the International Conference on Management of Data (SIGMOD’18), Gautam Das, Christopher M. Jermaine, and Philip A. Bernstein (Eds.). ACM, 1773--1776.
[22]
Meg Young, Luke Rodriguez, Emily Keller, Feiyang Sun, Boyang Sa, Jan Whittington, and Bill Howe. 2019. Beyond open vs. closed: Balancing individual privacy and public accountability in data sharing. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT’19). ACM, 191--200.

Cited By

View all
  • (2024)Asking MultiCrit Questions: A Reflexive and Critical Framework to Promote Health Data Equity for the Multiracial PopulationThe Milbank Quarterly10.1111/1468-0009.12696102:2(398-428)Online publication date: 29-Feb-2024
  • (2024)‘COVID-19 vaccines are safe’: however, the issues of vaccine equity and data equity remainCritical Public Health10.1080/09581596.2024.235259534:1(1-4)Online publication date: 29-May-2024
  • (2023)Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challengesData & Policy10.1017/dap.2023.65Online publication date: 27-Mar-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Digital Government: Research and Practice
Digital Government: Research and Practice  Volume 2, Issue 2
COVID-19 Commentaries and Case Study
April 2021
119 pages
EISSN:2639-0175
DOI:10.1145/3442345
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2021
Online AM: 05 February 2021
Accepted: 01 December 2020
Revised: 01 November 2020
Received: 01 September 2020
Published in DGOV Volume 2, Issue 2

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Data equity
  2. data ethics
  3. responsible data science

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)232
  • Downloads (Last 6 weeks)36
Reflects downloads up to 27 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Asking MultiCrit Questions: A Reflexive and Critical Framework to Promote Health Data Equity for the Multiracial PopulationThe Milbank Quarterly10.1111/1468-0009.12696102:2(398-428)Online publication date: 29-Feb-2024
  • (2024)‘COVID-19 vaccines are safe’: however, the issues of vaccine equity and data equity remainCritical Public Health10.1080/09581596.2024.235259534:1(1-4)Online publication date: 29-May-2024
  • (2023)Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challengesData & Policy10.1017/dap.2023.65Online publication date: 27-Mar-2023
  • (2022)Developing data capability with non-profit organisations using participatory methodsBig Data & Society10.1177/205395172210998829:1Online publication date: 11-May-2022
  • (2022)The Reordering of Everyday Life through Digital technologies During the Covid-19 PandemicProceedings of the 2022 International Conference on Information and Communication Technologies and Development10.1145/3572334.3572375(1-7)Online publication date: 27-Jun-2022
  • (2021)Racial Bias and ASWB Exams: A Failure of Data EquityResearch on Social Work Practice10.1177/1049731521105598632:3(255-258)Online publication date: 2-Dec-2021
  • (2021)COVID and social mediaACM SIGWEB Newsletter10.1145/3494825.34948302021:Autumn(1-20)Online publication date: 3-Dec-2021
  • (2021)Deciding If and How to Use a COVID-19 Contact Tracing App: Influences of Social Factors on Individual Use in JapanProceedings of the ACM on Human-Computer Interaction10.1145/34798685:CSCW2(1-30)Online publication date: 18-Oct-2021

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Full Access

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media