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Two main concerns are associated with this increase in facial recognition: (1) the fact that these systems are typically less accurate for marginalized groups, which can be described as \u201cbias\u201d, and (2) the increased surveillance through these systems. Our paper is concerned with the first issue. Specifically, we explore an intuitive technique for reducing this bias, namely \u201cblinding\u201d models to sensitive features, such as gender or race, and show why this cannot be equated with reducing bias. Even when not designed for this task, facial recognition models can deduce sensitive features, such as gender or race, from pictures of faces\u2014simply because they are trained to determine the \u201csimilarity\u201d of pictures. This means that people with similar skin tones, similar hair length, etc. will be seen as similar by facial recognition models. When confronted with biased decision-making by humans, one approach taken in job application screening is to \u201cblind\u201d the human decision-makers to sensitive attributes such as gender and race by not showing pictures of the applicants. Based on a similar idea, one might think that if facial recognition models were less aware of these sensitive features, the difference in accuracy between groups would decrease. We evaluate this assumption\u2014which has already penetrated into the scientific literature as a valid de-biasing method\u2014by measuring how \u201caware\u201d models are of sensitive features and correlating this with differences in accuracy. In particular, we blind pre-trained models to make them less aware of sensitive attributes. We find that awareness and accuracy do not positively correlate, i.e., that <jats:italic>bias<\/jats:italic><jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ne$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u2260<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula><jats:italic>awareness<\/jats:italic>. In fact, blinding barely affects accuracy in our experiments. The seemingly simple solution of decreasing bias in facial recognition rates by reducing awareness of sensitive features does thus not work in practice: trying to ignore sensitive attributes is <jats:italic>not<\/jats:italic> a viable concept for less biased FR.<\/jats:p>","DOI":"10.1007\/s43681-021-00108-6","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T09:03:19Z","timestamp":1635325399000},"page":"509-522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Bias, awareness, and ignorance in deep-learning-based face recognition"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2245-8066","authenticated-orcid":false,"given":"Samuel","family":"Wehrli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7639-2771","authenticated-orcid":false,"given":"Corinna","family":"Hertweck","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-6802","authenticated-orcid":false,"given":"Mohammadreza","family":"Amirian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7484-536X","authenticated-orcid":false,"given":"Stefan","family":"Gl\u00fcge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3784-0420","authenticated-orcid":false,"given":"Thilo","family":"Stadelmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"108_CR1","unstructured":"Jain, A.K., Li, S.Z.: Handbook of face recognition, vol. 1. 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