New White Paper Offers Ethical Guidelines for Procurement and Management of Smart City Technologies

By Brad Limov, 8/23/2024

OVERVIEW

The rapid advancement of smart city technologies, particularly the widespread deployment of camera-based surveillance systems, has sparked popular conversation about ethics, transparency, and accountability in data collection and use. Our white paper, “Being Watched: Best Practices for Embedding Ethics, Transparency, and Accountability in Smart City Surveillance Technologies,” offers a toolkit for municipal entities, community organizations, and other stakeholders to participate in the process of establishing policies for surveillance technology deployment and data management.

The white paper, which is part of our Being Watched project, provides four analytical frameworks to guide the development of ethical smart city policies:

Functional Analysis: Examines the core functions and activities in which smart city cameras are deployed, linking them to the overarching goals and values of the city.

Stakeholder Analysis: Identifies the various stakeholders impacted by or interested in smart city surveillance technologies and their data, and analyzes their interests, concerns, and values.

Risk Mitigation Analysis: Addresses the potential impact of surveillance technologies on civil rights and civil liberties, and the need for public access to records generated by smart cameras for transparency in government.

Data Life Cycle Analysis: Maps the journey of public data from creation to deletion, ensuring proper control and management throughout its lifecycle.

We call for a holistic view of data management, from creation to deletion, and a commitment to understanding and accommodating privacy concerns while aligning the use of these technologies with legitimate governmental purposes. For more information about our white paper and the Being Watched project, please contact Dr. Sharon Strover (sharon [dot] strover [at] austin.utexas.edu).

To download the white paper, click here.

Acknowledgement:

This work was supported by Good Systems, a research grand challenge at the University of Texas at Austin.