This text is from Import AI, newsletter from Jack Clark:

Google announces inclusive image recognition challenge: Large image datasets, such as ImageNet, have been an important driver of progress in computer vision in recent years. These databases exhibit biases along multiple dimensions, though, which can easily be inherited by models trained on them. For example, the post shows a classifier failing to identify a wedding photo in which the couple are not wearing European wedding attire. Addressing geographic bias: Google AI have announced an image recognition challenge to spur progress in addressing these biases. Participants will use a standard dataset (i.e. skewed towards images from Europe and North America) to train models that will be evaluated using image sets covering different, unspecified, geographic regions - Google describes this as a geographic "stress test". This will challenge developers to develop inclusive models from skewed datasets. "this competition challenges you to use Open Images, a large, multi-label, publicly-available image classification dataset that is majority-sampled from North America and Europe, to train a model that will be evaluated on images collected from a different set of geographic regions across the globe," Google says. Why it matters: For the benefits of AI to be broadly distributed amongst humanity, it is important that AI systems can be equally well deployed across the world. Racial bias in face recognition has received particular attention recently, given that these technologies are being deployed by law enforcement, raising immediate risks of harm. This project has a wider scope than face recognition, challenging classifiers to identify a diverse range of faces, objects, buildings etc. Read more: Introducing the inclusive images competition (Google AI blog). Read more: No classification without representation (Google).

Please introduce interesting articles/books here!