Case studies, such as , 2015 have shown that in image summarization, such as with Google Image Search, the people in the results presented for occupations are more imbalanced with respect to sensitive attributes such as gender and ethnicity than the ground truth. Most of the existing approaches to correct for this problem in image summarization assume that the images are labelled and use the labels for training the model and correcting for biases. However, these labels may not always be present. Furthermore, it is often not possible (nor even desirable) to automatically classify images by sensitive attributes such as gender or race. Moreover, balancing according to the labels does not guarantee that the diversity will be visibly apparent - arguably the only metric that matters when selecting diverse images. We develop a novel approach that takes as input a visibly diverse control set of images and uses this set to produce images in response to a query which is similarly visibly diverse. We implement this approach using pre-trained and modified Convolutional Neural Networks like VGG-16, and evaluate our approach empirically on the Image dataset compiled and used by , 2015. We compare our results with the Google Image Search results from , 2015 and natural baselines and observe that our algorithm produces images that are accurate with respect to their similarity to the query images (on par with that of the Google Image Search results), but significantly outperforms with respect to visible diversity as measured by their similarity to our diverse control set.
There are other works that attempt to ensure diversity in the learning algorithm without using gender or race labels. @cite_23 consider the problem of gender bias in word embeddings trained on Google News articles and provide methods to modify the embeddings to debias them. Through standard gender-related words, they identify the direction of the gender bias in given word embeddings and then attempt to remove it.
@cite_23
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The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between the words receptionist and female, while maintaining desired associations such as between the words queen and female. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.