Please use this identifier to cite or link to this item:
|Title:||Demographic Transparency to Combat Discriminatory Data Analytics Recommendations|
|Keywords:||Data Analytics;Discrimination;Demographic Transparency;Ethical Decision Making;Moral Intensity;Proximity|
|Abstract:||Data Analytics (DA) has been blamed for contributing to discriminatory managerial decisions in organizations. To date, most studies have focused on the technical antecedents of such discriminations. As a result, little is known about how to ameliorate the problem by focusing on the human aspects of decision making when using DA in organizational settings. This study represents an effort to address this gap. Drawing on the cognitive elaboration model of ethical decision-making, construal level theory, and the literature on moral intensity, this study investigates how the availability and the design of demographic transparency (a form of decisional guidance) can lower DA users’ likelihood of agreement with discriminatory recommendations of DA tools. In addition, this study examines the role of user’s mindfulness and organizational ethical culture on this process. In an experimental study users interact with a DA tool that provides them with a discriminatory recommendation. The results confirm that demographic transparency significantly impacts both recognition of the moral issue at hand and perceived proximity toward the subject of the decision, which in turn help decrease the likelihood of users’ approval of the discriminatory recommendation. Moreover, the results suggest that user’s mindfulness and organizational ethical culture enhance the positive impacts of demographic transparency.|
|Appears in Collections:||Open Access Dissertations and Theses|
Files in This Item:
|Ebrahimi_Sepideh_finalsubmission201808_PhD.pdf||2.14 MB||Adobe PDF||View/Open|
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.