In decision-making processes, it is important to identify and mitigate both conscious and unconscious (or \u201cimplicit\u201d) bias. The term \u201cbias\u201d denotes cognitive distortions, pre\u2011formed patterns of thought or predispositions that are automatically present to varying degrees in every individual and facilitate rapid, automatic processes of judgement. As research shows, however, they can distort evaluation and decision\u2011making processes both positively and negatively, and may therefore lead to discrimination \u2013 for example on the grounds of gender, origin, disability or chronic illness. Even in science and the humanities, which are by definition committed to facts and evidence, it is impossible to rule out bias. Decision\u2011making processes affected in this way are not science\u2011driven, however.<\/p>\n\n\n\n
Guideline\u202f3<\/strong> of the Code of Conduct builds on this point and calls for transparent procedures in staff selection and development, avoiding implicit bias as much as possible.<\/p>\n\n\n\n In research design, too, this can be an issue \u2013 according to Guideline\u202f9<\/strong>, applying methods to mitigate bias can form part of good research practice. <\/p>\n\n\n\n Guideline<\/strong>\u202f16<\/strong> identifies neutrality of review processes and discussions as an important basis for credibility, fairness and quality in scientific decision\u2011making processes.<\/p>\n\n\n\n Examples of bias<\/em><\/p>\n\n\n\n One example of \u201cstatus bias\u201d or the \u201chalo effect\u201d is provided by Huber et\u202fal. (2022)<\/em>. The authors demonstrate that the reputation of researchers can influence assessment in the peer review process. They submitted an article jointly authored by a renowned Nobel laureate in economics and a less well\u2011known researcher to a behavioural economics journal. The manuscript\u2019s success with reviewers varied depending on the information provided about the authors: when the Nobel laureate was named as the author, the identical manuscript received 77\u202fpercent approval; when the lesser\u2011known researcher was named, approval dropped to 35\u202fpercent; without any author names, approval was 52\u202fpercent.<\/p>\n\n\n\n In another study, Murray et\u202fal. (2019)<\/em> found that in invitations to submit full articles to a biology journal, manuscripts by male authors were preferred over those by female authors for publication in the journal in question. The difference was even greater when the review panel consisted exclusively of men.<\/p>\n\n\n\n Mitigating bias<\/em><\/p>\n\n\n\n In order to counteract distortions in judgement caused by non\u2011scientific factors, it is important to be aware of these processes and to engage in continuous critical self\u2011reflection in selection and decision\u2011making contexts.<\/p>\n\n\n\n The DFG provides a range of resources that are aimed at raising awareness of bias in scientific evaluation and decision-making processes:<\/p>\n\n\n
These resources are primarily aimed at DFG reviewers and committee members, but their content can also be applied to other scientific evaluation and decision-making processes such as peer review procedures, appointment procedures and recruitment processes. For this reason, use of these resources is open to all who wish to work towards avoiding bias.<\/p>\n\n\n\n\n