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Crucial Challenges in Large-Scale Black Box Analyses

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Advances in Bias and Fairness in Information Retrieval (BIAS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1418))

Abstract

To hold software service and platform providers accountable, it is necessary to create trustworthy, quantified evidence of problematic algorithmic decisions, e.g., by large-scale black box analyses. In this article, we summarize typical and general challenges that arise when such studies are conducted. Those challenges were encountered in multiple black box analyses we conducted, among others in a recent study to quantify, whether Google searches result in search results and ads for unproven stem cell therapies when patients research their disease and possible therapies online. We characterize the challenges by the approach to the black box analysis, and summarize some of the lessons we learned and solutions, that will generalize well to all kinds of large-scale black box analyses. While the studies we base this article on where one-time studies with an explorative character, we conclude the article with some challenges and open questions that need to be solved to hold software service and platform providers accountable with the help of permanent, large-scale black box analyses.

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Notes

  1. 1.

    These initial impressions were collected during the Wellcome Trust Seed project-funded workshop “Patienthood and Participation in the Digital Era: findings and future directions” hosted by the Usher Institute at the University of Edinburgh in August 2018. (Erikainen et al. [14]).

  2. 2.

    www.eurostemcell.org.

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Acknowledgment

The presented project EDD has been partially funded by the EU stem cell public engagement project, EuroStemCellFootnote 2 and by a generous grant from the University of Edinburgh School of Social and Political Science. The research was supported by the project GOAL “Governance of and by algorithms (Funding code 01IS19020) which is funded by the German Federal Ministry of Education and Research.

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Authors and Affiliations

  1. Algorithm Accountability Lab, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Tobias D. Krafft, Martin Reber, Roman Krafft & Katharina A. Zweig

  2. Science, Technology and Innovation Studies, University of Edinburgh, Edinburgh, Scotland, UK

    Anna Coutrier

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  1. Tobias D. Krafft
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  5. Katharina A. Zweig
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Correspondence to Tobias D. Krafft .

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  1. Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain

    Ludovico Boratto

  2. Unitelma Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  3. École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Mirko Marras

  4. University of L'Aquila, L’Aquila, Italy

    Giovanni Stilo

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Krafft, T.D., Reber, M., Krafft, R., Coutrier, A., Zweig, K.A. (2021). Crucial Challenges in Large-Scale Black Box Analyses. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2021. Communications in Computer and Information Science, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-78818-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-78818-6_13

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