Xiang gives the example of IBM’s “Diversity in Faces” (“ DiF”) dataset which was initially received positively for being far more diverse and balanced than previous face image datasets. There has been a troubled history of AI developers trying to address bias problems by ‘harvesting’ faces of real people to expand and balance out AI’s training data. Past solutions have run aground on privacy concerns For example, AI is more likely to incorrectly predict that an individual in a photo is female if the background is indoors and the reverse for outdoor images, perpetuating long-standing stereotypes of women inhabiting domestic spheres and men inhabiting public spheres. On the second problem, AI can make spurious correlations because it ‘divines’ misleading patterns from the training data, often due to societal biases in the training data. AI has much the same problem: algorithms developed in Western countries perform better for Caucasian faces while conversely algorithms developed in East Asian countries perform better for East Asian faces. On the first problem, humans have trouble recognising faces of races other than their own if they have not had enough exposure to those other races: called the “other race problem” or less politely, “they all look the same to me”. Algorithmic bias boils down to two problems: (1) lack of representation and (2) spurious correlations. Reducing bias in HCCV systems requires collecting a large, diverse, and candid image datasets, which can run counter to privacy protections.īut as Xiang points out, bias in HCCV systems is more nuanced than not seeing enough faces. Studies have attributed the biases in HCCV AI to a lack of diversity in the datasets used to train these commercial AI systems. Xiang applies her analysis broadly to ‘human-centric computer vision ( HCCV)’ which she defines as AI systems that rely on images of humans for their training and test data. In her Harvard Law and Technology Journal article, Alice Xiang, Sony’s Global Head of AI Ethics, argues that, in the headlong rush to regulate FRT, there is “n under-appreciated tension between privacy protections and bias mitigation efforts”.

Hard Aground . . . Again by Staff