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An Assessement Of Algorithmic Accountability Methods

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  • Cristina VOINEA
  • Radu USZKAI

Abstract

The aim of this paper is to map and assess some of the solutions that have been put forth to problems like algorithmic opacity or algorithmic discrimination. We will focus mainly on algorithmic transparency – understood as the unconditional disclosure of the data algorithms are working on as well as of their inner workings – due to the prevalence of this solution in the literature (see, for example, (Brauneis & Goodman, 2017) (Diakopoulos & Koliska, 2017) (Zarsky, 2016). Transparency is the main type of accountability of algorithmic advanced by various researchers or digital rights activists. After critically assessing the practical consequences and some of the implications of making algorithms transparent, we claim that what is lacking in the online environment is trust in automated decision-making systems. We see trust as resting at the basis of accountability methods and claim that the main issue with transparency is that it cannot restore trust. As such, the edifices of accountability mechanisms will eventually collapse if they do not obtain their legitimacy from the trust that has been invested in them.

Suggested Citation

  • Cristina VOINEA & Radu USZKAI, 2018. "An Assessement Of Algorithmic Accountability Methods," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(1), pages 933-938, November.
    • Handle: RePEc:rom:mancon:v:12:y:2018:i:1:p:933-938
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    References listed on IDEAS

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    2. James B. Pick & Avijit Sarkar, 2015. "The Global Digital Divides," Progress in IS, Springer, edition 127, number 978-3-662-46602-5, June.
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