Building Ethics into Privacy Frameworks for Big Data and AI

Abstract

Big data, new technologies, and new analytical approaches, if applied responsibly, have tremendous potential to be used for the public good. Big data’s greatest value for global development lies in harnessing the power of real-time and predictive analytics for smarter decision making, anticipatory approaches to managing risk, and new ways to measure social impact. At the same time, big data amplifies risks to privacy, fairness, equality, and due process. Large-scale data collection can expose characteristics and behaviors of individuals, lead to biased decision-making based on unrepresentative or inaccurate data samples, and lack transparency, preventing individuals from exercising due process rights.

UN Global Pulse and the International Association of Privacy Professionals explored these issues and discussed their many aspects at a jointly hosted event: “Building a Strong Data Privacy and Ethics Program: From Theory to Practice,” held in May 2017 (hereafter referred to as the “UN GP/IAPP event”).

This report provides an overview of how organizations can operationalize data ethics, drawing on the discussions at the UN GP/IAPP event as well as on additional research about data ethics and privacy best practices in a world of big data analytics. It looks at how organizations deploying data analytics and artificial intelligence can reflect ethics considerations in their decision making, borrowing tried and true operational tactics from the field of information privacy. Such steps may include, for example: building a multi-disciplinary team across departments to practice ethics “on the ground”; conducting ethics assessments for new big data projects to consider their personal and societal impacts, while consulting with external and internal ethics working groups; and building programs that are scalable and flexible, which depend on factors such as the societal context of a big data project and the organizational structure of the entity performing it.

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