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Data science data governance
Kroll J.  IEEE Security and Privacy 16 (6): 61-70, 2018. Type: Article
Date Reviewed: Sep 23 2019

Recent advances in machine learning, data analytics, and artificial intelligence (AI) have empowered human beings to automatically make decisions by processing vast amounts of data much more efficiently than ever before. Along with great advantages, data-driven automated decision making also introduces a broad range of ethical challenges for organizations and practitioners. These ethical challenges involve “data security, privacy, avoidance of undue discrimination, accountability, and transparency.”

Facing the enormous challenges in this ethical evolution, practitioners and organizations very much need an up-to-date summary of principles, guidelines, best practices, and recommendations on data governance. After a brief introduction, this paper provides a number of emerging best practices and concrete recommendations: “minimize data collected and retained”; data should be encrypted at rest and in transit”; “scrub or aggregate retained data when possible”; “constantly question the responsibility of ... methods and findings”; “designate and empower a data use review board”; “write and publish data-focused social impact statements”; “attempt to explain data-driven processes to breed understanding”; “look for systematic biases in data” and “potential cases of unfairness”; “consider not just successes but errors and feedback loops”; “design systems to allow the challenge and correction ... by humans”; and so on.

This paper also provides a summary of current global efforts to establish data responsibility principles and standards, including the Dagstuhl principles for “accountable algorithms,” the ACM US Policy Council’s principles for “algorithmic transparency and accountability,” the European Union (EU) General Data Protection Regulation (GDPR), the IEEE Global Initiative, IEEE Standard P7003, and so on.

This paper should definitely benefit people working in the cybersecurity field. A 2015 ACM Turing Award winner, Martin Hellman lectured on “The Technological Imperative for Ethical Evolution” at the 69th Lindau Nobel Laureate Meeting, in July 2019 [1]. Regardless of protecting others or protecting oneself in the Information Age, good data governance is important. Therefore, anyone interested in the ethical evolution will enjoy reading this paper, which summarizes current best practices, concrete recommendations, and global efforts related to data governance principles and standards.

Reviewer:  Chenyi Hu Review #: CR146703 (1912-0453)
1) Hellman, M. E. “The Technological Imperative for Ethical Evolution,” (Accessed Sep. 20, 2019).
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