TY - JOUR
T1 - Introducing contextual transparency for automated decision systems
AU - Sloane, Mona
AU - Solano-Kamaiko, Ian René
AU - Yuan, Jun
AU - Dasgupta, Aritra
AU - Stoyanovich, Julia
N1 - Funding Information:
This research was supported in part by National Science Foundation awards 1916505, 1922658 and 1928627.
Publisher Copyright:
© 2023, Springer Nature Limited.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
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U2 - 10.1038/s42256-023-00623-7
DO - 10.1038/s42256-023-00623-7
M3 - Article
AN - SCOPUS:85149914148
SN - 2522-5839
VL - 5
SP - 187
EP - 195
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 3
ER -