Aug 7, 2023 · Why Is There Bias In Facial Recognition Tools? Cameras powered by algorithms repeatedly struggle with dark skin — from self-driving cars less accurately detecting dark-skinned pedestrians to Google Photos mislabeling Black people as gorillas.

Dec 13, 2023 · In this excerpt, Buolamwini discusses how datasets used to train facial recognition systems can lead to bias and how even datasets considered benchmarks, including one created by a government agency that set out to collect a diverse dataset, can …

Nov 3, 2020 · Racial bias is most prevalent in the selection of images used to train the algorithm. In general, result accuracy is proportional to data quality, and a racially unbiased technology would require equal racial representation within the dataset.

The own-race bias (ORB) is a reliable phenomenon across cultural and racial groups where unfamiliar faces from other races are usually remembered more poorly than own-race faces (Meissner and Brigham, 2001). By adopting a yes–no recognition ...

Oct 24, 2020 · More disturbingly, however, the current implementation of these technologies involves significant racial bias, particularly against Black Americans. Even if accurate, face recognition empowers a law enforcement system with a long history of racist and anti-activist surveillance and can widen pre-existing inequalities.

Dec 19, 2019 · How accurately do face recognition software tools identify people of varied sex, age and racial background? According to a new study by the National Institute of Standards and Technology (NIST), the answer depends on the algorithm at the heart of the system, the application that uses it and the data it’s fed — but the majority of face ...

Oct 27, 2021 · 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 “bias”, and (2) the increased surveillance through these systems. Our paper is concerned with the first issue.

Jan 29, 2019 · On Sunday, a study from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrated the effectiveness of a new algorithm for mitigating biases in a face detection...

Feb 16, 2020 · We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups. We show variations in the optimal scoring threshold for face-pairs across different subgroups.

Mar 27, 2019 · Facial recognition systems made by Microsoft, IBM and Face++ had a harder time identifying the gender of dark-skinned women like African-Americans compared with white men, according to a study...