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Introducing Contextual Transparency for Automated Decision Systems

This paper has been published in Nature Machine Intelligence. The authors of this paper are:

    Mona Sloane      Research Assistant Professor, Tandon School of Engineering, New York University, USA
  • Ian René Solano-Kamaiko       PhD student, School of Computing and Information Science, Cornell Tech, USA
  • Jun Yuan       PhD student, Department of Data Science, New Jersey Institute of Technology, USA
  • Aritra Dasgupta       Assistant Professor, Department of Data Science, New Jersey Institute of Technology, USA
  • Julia Stoyanovich       Associate Professor, Tandon School of Engineering and Center for Data Science, New York University, USA

Abstract

Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio-technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy-aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy-preserving data visualization has witnessed progress along many of these dimensions. In this state-of-theart report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road-map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio-technical challenges involving data privacy with the help of visualization.


Fig: Recruiting process.

Online Access

BibTeX Reference

        @article{sloane2023introducing,
          title={Introducing contextual transparency for automated decision systems},
          author={Sloane, Mona and Solano-Kamaiko, Ian Ren{\'e} and Yuan, Jun and Dasgupta, Aritra and Stoyanovich, Julia},
          journal={Nature Machine Intelligence},
          volume={5},
          number={3},
          pages={187--195},
          year={2023},
          publisher={Nature Publishing Group UK London}
        }
    }