%0 Conference Paper %1 10.1007/978-3-030-78818-6_13 %A Krafft, Tobias D. %A Reber, Martin %A Krafft, Roman %A Coutrier, Anna %A Zweig, Katharina A. %B Advances in Bias and Fairness in Information Retrieval %C Cham %D 2021 %E Boratto, Ludovico %E Faralli, Stefano %E Marras, Mirko %E Stilo, Giovanni %I Springer International Publishing %K blackbox challenges large xai %P 143--155 %T Crucial Challenges in Large-Scale Black Box Analyses %X 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. %@ 978-3-030-78818-6

@inproceedings{10.1007/978-3-030-78818-6_13, 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.}, added-at = {2021-06-27T12:04:44.000+0200}, address = {Cham}, author = {Krafft, Tobias D. and Reber, Martin and Krafft, Roman and Coutrier, Anna and Zweig, Katharina A.}, biburl = {https://www.bibsonomy.org/bibtex/2abb0c331e56f8b48a78c22d8443d51a2/tomhanika}, booktitle = {Advances in Bias and Fairness in Information Retrieval}, editor = {Boratto, Ludovico and Faralli, Stefano and Marras, Mirko and Stilo, Giovanni}, interhash = {b021b34e57517609654aaa79e97e716e}, intrahash = {abb0c331e56f8b48a78c22d8443d51a2}, isbn = {978-3-030-78818-6}, keywords = {blackbox challenges large xai}, pages = {143--155}, publisher = {Springer International Publishing}, timestamp = {2021-06-27T12:04:44.000+0200}, title = {Crucial Challenges in Large-Scale Black Box Analyses}, year = 2021 }