TY - JOUR
T1 - Transparency, fairness, data protection, neutrality
T2 - Data management challenges in the face of new regulation
AU - Abiteboul, Serge
AU - Stoyanovich, Julia
N1 - Funding Information:
This work was supported in part by National Science Foundation (NSF) Grant No. 1741047, and by Agence Nationale de la Recherche (ANR) Grant Headwork. Authors’ addresses: S. Abiteboul, Inria, ENS, Paris, Serge Abiteboul, Institut National de Recherche en Informatique et Au-tomatique, École normale supérieure, PSL University, 75005 Paris, France; email: Serge.Abiteboul@inria.fr; J. Stoyanovich, Department of Computer Science and Engineering, Tandon School of Engineering, New York University, 370 Jay Street, Brooklyn, NY 11201 USA; email: stoyanovich@nyu.edu. 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 permissions@acm.org. © 2019 Association for Computing Machinery. 1936-1955/2019/06-ART15 $15.00 https://doi.org/10.1145/3310231
Funding Information:
We discuss three recent frameworks: the European Union’s General Data Protection Regulation (GDPR) (The European Union 2016), the New York City Automated Decisions Systems (ADS) Law (The New York City Council 2017), and the Net Neutrality principle. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. The GDPR and the NYC ADS Law aim to protect the rights of individuals who are impacted by data collection and analysis, while the Net Neutrality principle ensures that services are being treated equitably. Yet, despite the focus on organizations, rights of individuals also figure prominently in the neutrality debate: One of the imperatives is that individuals should be able to enjoy freedom of choice and expression on-line. We will give some legal context on neutrality by discussing the EU Regulation 2015/2120 (The European Parliament AND Council 2015), the Indian Net Neutrality Regulatory Framework (Government of India, Ministry of Communications 2018), and the ongoing regulatory debate on Net Neutrality in the U.S.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7
Y1 - 2019/7
N2 - The data revolution continues to transform every sector of science, industry, and government. Due to the incredible impact of data-driven technology on society,we are becoming increasingly aware of the imperative to use data and algorithms responsibly-in accordance with laws and ethical norms. In this article, we discuss three recent regulatory frameworks: the European Union's General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, which aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. Our goal in this article is to bring these regulatory frameworks to the attention of the data management community and to underscore the technical challenges they raise and that we, as a community, are wellequipped to address. The main takeaway of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.
AB - The data revolution continues to transform every sector of science, industry, and government. Due to the incredible impact of data-driven technology on society,we are becoming increasingly aware of the imperative to use data and algorithms responsibly-in accordance with laws and ethical norms. In this article, we discuss three recent regulatory frameworks: the European Union's General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, which aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. Our goal in this article is to bring these regulatory frameworks to the attention of the data management community and to underscore the technical challenges they raise and that we, as a community, are wellequipped to address. The main takeaway of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.
KW - Data protection
KW - Fairness
KW - Neutrality
KW - Responsible data science
KW - Transparency
UR - http://www.scopus.com/inward/record.url?scp=85071111403&partnerID=8YFLogxK
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U2 - 10.1145/3310231
DO - 10.1145/3310231
M3 - Article
AN - SCOPUS:85071111403
SN - 1936-1955
VL - 11
JO - Journal of Data and Information Quality
JF - Journal of Data and Information Quality
IS - 3
M1 - 15
ER -