How accurate are facial technologies ? How does face veriﬁcation work? Facial technology AI systems are trained on big datasets. Despite their size, these datasets often lack the variety of genders and skin tones we can observe in real life, caus- ing the system to be less accurate for some people. Studies show that those with intersec- tional identities are especially affected. These two key errors reduce accuracy light medium dark OK <Same> dataset average dataset average Intersectional qualities magnify biases in algorithms Intersectionality noun The theory that people who have multiple social identities, like race, gender, sexuality, and class, that are different from the norm often experience combined systemic oppression and discrimination. Face veriﬁcation is used to conﬁrm that the person in the document is the same as the one present. For this, it needs a still image of the face and a new photo taken on the spot. Step 1. Step 2. Step 3. Step 4. Step 5. AI detects the faces in the images. It compares the size and distance of facial features. It decides if the faces are similar enough to be considered the same. By analyzing facial features, AI translates the data into information it can calculate with. [ in-ter-sek-shuh-nal-i-tee ] gender nationality socioeconomic status class sexual orientation race the ‘norm’ deviation from the ‘norm’ MAMMOth Project – Grant ID 101070285] Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reﬂect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them. Scan the QR code to see the references for this material. This poster was designed by Dali is a science communicator. Their expertise is in the ﬁeld of AI ethics and biases. They enjoy creating and curating memorable experiences for a variety of audiences. Dali Fekete (he/she/they) linkedin.com/in/dalifekete Cristina is a graphic designer and science communicator. She combines her passion for science with her passion for art, creating wonderful illustrations, infographics, and other visual materials for scientiﬁc dissemination. Cristina Pop cristinapop.ro linkedin.com/in/cristinapopscicomm AI Biases in Face Recognition Facial technologies rely on AI algorithms to detect, recognize, verify, and identify people in photographs and videos. While it may sound sci-ﬁ, these technologies are already used by companies and governments for face veriﬁcation. KO <Different> The chance that AI will incorrectly allow access to an unauthorised person. False Acceptance Rate False Rejection Rate The chance that AI will wrongly deny access to a legitimate user. light medium pimples scars surgeries accidents ageing medication & serious illnesses dark AI systems often struggle to understand these differences, which then reduces the accuracy of correct veriﬁcation. This shortcoming can affect everyone, regardless of their (intersectional) characteristics. All faces change over time INTERNATIONAL PASSPORT P<NLRUGCLT<<CRISTINA<POP<<DALI<FEKETE<<<<<<<<<<<<<<<<< 04062024<<DSEC<<M4D3W1THL0V3<<<<<<<<<<<<<<<<<<<<<<<< VIDEO PHOTO PORTRAI VIDEO PHOTO PORTRAI INTERNATIONA P<NLRUGCLT<<CRISTINA<POP<<DALI< 04062024<<DSEC<<M4D3W1THL0V3<< Darker female Darker male Lighter female Lighter male Microsoft Face++ IBM Amazon Kairos 20.8% Face Recognition Technology Difference between lowest and highest accuracy Accuracy (%) 33.7% 34.4% 31.4% 22.5% gender race religion Skin tone & gender Dark-skinned women who wear religious head coverings like scarves and hijabs face three challenges: their skin tone, their gender, and their apparel. The accuracy score is calculated by the ratio of correct and incorrect AI decisions. Incorrect decisions are often disproportionately large for intersectional communities in comparison to ‘the norm’, as shown in the chart below. 100110101010 100101101010 001110010101 010101000101 100110101010 1001