TY - JOUR
T1 - Responsible Data Management
AU - Stoyanovich, Julia
AU - Howe, Bill
AU - Jagadish, H. V.
N1 - Funding Information:
The work of Julia Stoyanovich was supported in part by NSF Grants No. 1926250, 1934464, and 1922658. The work of Bill Howe was supported in part by NSF Grants No. 1740996 and 1934405. The work of H.V. Jagadish was supported in part by NSF Grants No. 1741022 and 1934565.
Publisher Copyright:
© VLDB Endowment. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The need for responsible data management intensifies with the growing impact of data on society. One central locus of the societal impact of data are Automated Decision Systems (ADS), socio-legal-technical systems that are used broadly in industry, non-profits, and government. ADS process data about people, help make decisions that are consequential to people’s lives, are designed with the stated goals of improving efficiency and promoting equitable access to opportunity, involve a combination of human and automated decision making, and are subject to auditing for legal compliance and to public disclosure. They may or may not use AI, and may or may not operate with a high degree of autonomy, but they rely heavily on data. In this article, we argue that the data management community is uniquely positioned to lead the responsible design, development, use, and oversight of ADS. We outline a technical research agenda that requires that we step outside our comfort zone of engineering for efficiency and accuracy, to also incorporate reasoning about values and beliefs. This seems high-risk, but one of the upsides is being able to explain to our children what we do and why it matters.
AB - The need for responsible data management intensifies with the growing impact of data on society. One central locus of the societal impact of data are Automated Decision Systems (ADS), socio-legal-technical systems that are used broadly in industry, non-profits, and government. ADS process data about people, help make decisions that are consequential to people’s lives, are designed with the stated goals of improving efficiency and promoting equitable access to opportunity, involve a combination of human and automated decision making, and are subject to auditing for legal compliance and to public disclosure. They may or may not use AI, and may or may not operate with a high degree of autonomy, but they rely heavily on data. In this article, we argue that the data management community is uniquely positioned to lead the responsible design, development, use, and oversight of ADS. We outline a technical research agenda that requires that we step outside our comfort zone of engineering for efficiency and accuracy, to also incorporate reasoning about values and beliefs. This seems high-risk, but one of the upsides is being able to explain to our children what we do and why it matters.
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U2 - 10.14778/3415478.3415570
DO - 10.14778/3415478.3415570
M3 - Article
AN - SCOPUS:85100411135
SN - 2150-8097
VL - 13
SP - 3474
EP - 3488
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
ER -