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Accepted Paper:

Friction in explainable AI. Materialities of value in algorithmic credit scoring  
Miriam Fahimi (University of Klagenfurt) Katharina Kinder-Kurlanda (University of Klagenfurt)

Short abstract:

In this paper, we position value as material. Drawing on ethnographic research in a transparency project of a credit scoring agency, we argue that friction - the force when two socio-technical objects touch - occurs within the temporally situated materialities of value.

Long abstract:

Given that the making, implementation, and use of Artificial Intelligence (AI) are mostly opaque, transparency of AI is increasingly demanded by political, regulative, and private actors. These actors have different interests and ideas of how a value such as transparency can and should be aligned with proprietary AI. In this paper, we trace how this controversy was taken up by a private credit agency and addressed as a value conflict between trade secrecy and transparency. Based on our long-term ethnographic research, we follow the credit agency’s attempts to align transparency with their automated scoring system, and already existing practices of trade secrecy. Their transparency solution was to design a publicly available explainable AI tool to provide information and explanation about the scoring algorithm. Interested in AI transparency as a set of human and non-human practices, this paper pays attention to moments of friction during value alignment. We discuss friction not only occurring as a conflict between values but also in the materialities of value. In particular, friction emerges in the materialities of opaque pasts, unequal presents, and uncertain futures, and creates novel relations between transparency, trade secrecy, objects, and the positions of actors in the field. Based on our findings, we conclude by highlighting the importance of such situations where such relations are re-arranged. They reveal that the closure of value alignment and the success of technical fixes can only temporarily persist.

Traditional Open Panel P287
Beyond value alignment: invoking, negotiating and implementing values in algorithmic systems
  Session 1 Tuesday 16 July, 2024, -