The main goal of this short paper is to inform the neural art community at large on the ethical ramifications of using models trained on the imagenet dataset, or using seed images from classes 445 -n02892767- [’bikini, two-piece’] and 459- n02837789- [’brassiere, bra, bandeau’] of the same. We discovered that many of the images belong to these classes were verifiably pornographic, shot in a non-consensual setting, voyeuristic and also entailed underage nudity. Akin to the \textit{ivory carving-illegal poaching} and \textit{diamond jewelry art-blood diamond} nexuses, we posit there is a similar moral conundrum at play here and would like to instigate a conversation amongst the neural artists in the community. The emergence of tools such as BigGAN [1] and GAN-breeder [2] has ushered in an exciting new flavor of generative digital art [3] , generated using deep neural networks (See [4] for a survey). A cursory search on twitter 1 reveals hundreds of interesting art-works created using BigGANs. There are many detailed blog-posts 2 on generating neural art by beginning with seed images and performing nifty experiments in the latent space of BigGANs. At the point of authoring this paper, (8 September 2019, 4 :54 PM PST),users on the GanBreeder app 3 had discovered 49652500 images. Further, Christie's, the British auction house behemoth, recently hailed the selling of the neural network generated Portrait of Edmond Belamy for an incredible $432, 500 as signalling the arrival of AI art on the world auction stage [5] . Given the rapid growth of this field, we believe this to be the right time to have a conversation about a particularly dark ethical consequence of using such frameworks that entail models trained on the ImageNet dataset which has many images that are pornographic, non-consensual, voyeuristic and also entail underage nudity. We argue that this lack of consent in the seed images used to train the models trickles down to the final artform in a way similar to the blood-diamond syndrome in jewelry art [6] . An example: Consider the neural art image in Fig 1 we generated using the GanBreeder app. On first appearance, it is not very evident as to what the constituent seed classes are that went into the creation of this neural artwork image. When we solicited volunteers online to critique the artwork (See the collection of responses (Table 2) in the supplementary material), none had an inkling regarding a rather sinister trickle down effect at play here. As it turns out, we craftily generated this image using hand-picked specific instances of children images emanating from what we will showcase are two problematic seed image classes: Bikini and Brassiere. More specifically, for this particular image, we set the Gene weights to be: [Bikini: 42.35, Brassiere: 31.66, Comic Book -84.84 ]. We'd like to strongly emphasize at this juncture that the problem does not emanate from a visual patriarchal mindset [7] , whereby we associate female undergarment imagery to be somehow unethical, but the root cause lies in the fact that many of the images were curated into the dataset (at least with regards to the 2 above mentioned classes) were voyeuristic, pornographic, non-consensual and also entailed underage nudity. 2 Root cause: Absence of referencing consent during the curation of the imagenet dataset The emergence of the ImageNet dataset is widely considered to be a pivotal moment 4 in the deep learning revolution that transformed the domain computer vision. Two highly cited papers (with more than 10000 citations each), [8] authored by Deng et al in 2009 and [9] authored by Russakovsky et al in 2015, provide deep insights into the procedure used to curate the dataset. In the 2009 paper, subsections 3.1-Collecting Candidate Images and 3.2-Cleaning Candidate Images are dedicated towards the algorithms used to collect and clean the dataset and also to elucidate the specific ways in which the Amazon Mechanical Turk (AMT) platform was harnessed to scale the dataset. Similarly the entirety of Section-3-Dataset construction at large scale in [9] is dedicated towards extending the procedures for the 2015 release. It is indeed disappointing that neither the 2009 nor the 2015 versions of the endeavors required the AMT workers to check if the images they were asked to categorize and draw bounding boxes over, were ethically viable for usage. More specifically, in imagery pertaining to anthropocentric content, such as undergarment clothing, there was no attempt made towards assessing if the images entailed explicit consent given by the people in the images. In fact, none of the following words in the set [ethics, permission, voyeurism, consent] are mentioned in either of the two papers. As such, we have a plethora of images specifically belonging to the two categories detailed in Table 1 , that have serious ethical shortcomings. In Fig 2, we showcase the gallery of images from the two classes categorized into four sub-categories: Non-consensual/Voyeuristic, Personal, Verifiably pornographic and Underage / Children. In Fig 2, we also include images that were also incorrectly categorized (Specifically there was no brassieres being sported by the subjects in the images) (Sub-figure (a) ) and those that involved male subjects indulging in lecherous tomfoolery (Sub-figure (e) ). In this paper, we expose a certain unethical facet of neural art that emerges from usage of nonconsensual images that are present in certain specific classes of the imagenet dataset. These images born out of an unethical (and in some cases, illegal) act of voyeuristic non-consensual photography predominantly targeting women (as well as children), might implicitly poison the sanctity of the artworks that eventually emerge. This work is complementary to works such as [10; 11] that have explored the unspoken ethical dimensions of harnessing cheap crowd-sourced platforms such as AMT for scientific research in the first place, which we firmly believe is also an important issue to be considered.
There's non-consensual and pornographic images in the ImageNet dataset
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