Otterbacher, JahnaJahnaOtterbacher2024-12-132024-12-13201810.1007/978-3-319-98932-7_11https://crisdev.ouc.ac.cy/handle/3000/8265Journalists and researchers alike have claimed that IR systems are socially biased, returning results to users that perpetuate gender and racial stereotypes. In this position paper, I argue that IR researchers and in particular, evaluation communities such as CLEF, can and should address such concerns. Using as a guide the Principles for Algorithmic Transparency and Accountability recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social biases in IR systems and in particular, search engines. � Springer Nature Switzerland AG 2018.enConference Paper