Introducing contextual transparency for automated decision systems
Document Type
Article
Publication Date
3-1-2023
Abstract
As automated decision systems (ADS) get more deeply embedded into business processes worldwide, there is a growing need for practical ways to establish meaningful transparency. Here we argue that universally perfect transparency is impossible to achieve. We introduce the concept of contextual transparency as an approach that integrates social science, engineering and information design to help improve ADS transparency for specific professions, business processes and stakeholder groups. We demonstrate the applicability of the contextual transparency approach by using it for a well-established ADS transparency tool: nutritional labels that display specific information about an ADS. Empirically, it focuses on the profession of recruiting. Presenting data from an ongoing study about ADS use in recruiting alongside a typology of ADS nutritional labels, we suggest a nutritional label prototype for ADS-driven rankers such as LinkedIn Recruiter before closing with directions for future work.
Identifier
85149914148 (Scopus)
Publication Title
Nature Machine Intelligence
External Full Text Location
https://doi.org/10.1038/s42256-023-00623-7
e-ISSN
25225839
First Page
187
Last Page
195
Issue
3
Volume
5
Grant
1916505
Fund Ref
National Science Foundation
Recommended Citation
Sloane, Mona; Solano-Kamaiko, Ian René; Yuan, Jun; Dasgupta, Aritra; and Stoyanovich, Julia, "Introducing contextual transparency for automated decision systems" (2023). Faculty Publications. 1868.
https://digitalcommons.njit.edu/fac_pubs/1868