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 - 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
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UR - http://www.scopus.com/inward/citedby.url?scp=85071111403&partnerID=8YFLogxK
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 -