Algorithmic content moderation manages an explosive number of user-created content shared online everyday. Despite a massive number of 3D designs that are free to be downloaded, shared, and 3D printed by the users, detecting sensitivity with transparency and fairness has been controversial. Although sensitive 3D content might have a greater impact than other media due to its possible reproducibility and replicability without restriction, prevailed unawareness resulted in proliferation of sensitive 3D models online and a lack of discussion on transparent and fair 3D content moderation. As the 3D content exists as a document on the web mainly consisting of text and images, we first study the existing algorithmic eforts based on text and images and the prior endeavors to encompass transparency and fairness in moderation, which can also be useful in a 3D printing domain. At the same time, we identify 3D specific features that should be addressed to advance a 3D specialized algorithmic moderation. As a potential solution, we suggest a human-in-the-loop pipeline using augmented learning, powered by various stakeholders with diferent backgrounds and perspectives in understanding the content. Our pipeline aims to minimize personal biases by enabling diverse stakeholders to be vocal in reflecting various factors to interpret the content. We add our initial proposal for redesigning metadata of open 3D repositories, to invoke users' responsible actions of being granted consent from the subject upon sharing contents for free in the public spaces.
served an explosive number of user-created content posted everyday from Twitter to YouTube to Instagram and more. Following the acceleration of online contents which becomes even faster partly due to COVID-19, it has also become easier for people to access sensitive content that may not be appropriate for the general purpose. Owing to the scale of these content and users’ abilities to share and repost them in a flash, it becomes extremely costly to detect the sensitive content solely by manual work. Current social media platforms have adopted various (semi)automated content moderation methods including a deep learning-based classification (e.g., Microsoft Azure Content Moderator [1], DeepAI’s Nudity Detection API [2], Amazon Rekognition Content Moderation [3]).
Meanwhile, since desktop 3D printers have been flooded into the consumer market, 3D printing specific social platforms such as Thingiverse [4] have also gained popularity, contributing to the proliferation of shared 3D contents that are easily downloadable and loop validation pipeline using augmented replicable among community users. Despite learning that incrementally trains the model a massive number of 3D contents shared for with the input from the human workforce. free to date—As of 2020 2Q, there are near We highlight potential biases that are likely 1.8 million 3D models available for down- to be propagated from diferent perspectives load, excluding empty entries due to post of human moderators who provide final dedeletion—, there has been relatively little at- cisions and labeling for re-training a classitention to sensitive 3D contents. This might fication model. To mitigate those biases, we result in not only a lack of a dataset to be propose an image annotation interface to deused as a bench mark, but also a lack of dis- velop an explainable dataset and the system cussion on fair rationales to be utilized in that reflects various stakeholders’ perspecbuilding a algorithmic 3D content modera- tives in understanding the 3D content. We tion that integrates everyone’s perspectives conclude with initial recommendations for with a diferent background. Along with sig- metadata design to (1) require consent and (2) nificant advances in technology of machine inform previously unaware users of consent mechanisms and materials (e.g., 3D print- for publicizing the content which might ining in metals), the 3D printing community vade copyright or privacy. may present an even greater impact from the spread of content due to its limitless potential for replication and reproduction. In 2. Algorithmic Content view of various stakeholders who have difer- Moderation ent perspectives in consuming and interpreting contents—from K-12 teachers who may Manual moderation relying on a few trusted seek 3D files online to design curricula to human workforce and voluntary reports has artists who depict their creativity in digi- been common solutions to review shared tized 3D sculptures—, moderating 3D content contents. Unfortunately, it becomes inwith fairness becomes more challenging. 3D creasingly dificult to meet the demands of contents online often consist of images and growing volumes of users and user-created text that are possibly useful to adopt exist- content [12]. Algorithmic content moderaing moderation schemes including text (e.g., tion has taken an important place in popu[5, 6, 7, 8]) or image based (e.g., [9, 10, 11]) ap- lar social media platforms to prevent variproaches. However, there exist 3D printing ous sensitive content in real-time, including specific features (e.g., print support to avoid graphic violence, sexual abuse, harassment, overhangs, uni-colored outcome, segmented and more. As with other media posts, 3D in parts, etc.) that may prevent direct adop- contents available online appear as web doction of those schemes, requiring further con- uments that consist of images and text. For sideration about implementing advanced 3D example, to attract audiences and help othcontent moderation techniques. ers understand the design project, creators in
In this work, we first study the existing Thingiverse voluntarily include various incontent moderation eforts that has poten- formation such as written descriptions of the tial to be used in 3D content moderationand model, tags, as well as photos of a 3D printed discuss shared concerns in examining trans- design; thus, 3D content can provide us an parency and fairness issues in algorithmic ample opportunity to employ the existing content moderation. As a potential solution, text and image based moderation schemes. we propose a semi-automated human-in-the
Among various text-based solutions, sen- 2.1. Challenges in Moderating timent analysis is one traditionally popular 3D Content approach that categorizes input text into either two or more categories: positive and As we noted earlier, 3D contents appear as negative, or more detailed n-point scales web documents that consist of text descrip(e.g., highly positive, positive, neutral, neg- tions, auto-generated preview images, and ative, highly negative) [5, 6]. Moderators can user-uploaded images to help others comconsider categorization results in deciding prehend the content at a glance. Although whether the content is ofensive or discrim- it is technically possible to utilize existing inatory [13]. Various classifiers, such as Lo- text and image based moderation schemes, gistic Regression Model, Support Vector Ma- 3D models have unique features that make it chine, and random forest, are actively used in hard to directly adopt the existing CV techdetecting misogynistic posts on Twitter (e.g., niques to their rendered images or photos. [7, 8]). Jigsaw and Google’s Counter Abuse Technology suggested Perspective API [14] 2.1.1. 3D specific features that hamper provide a score on how toxic (i.e., rude, disre- the use of existing CV techniques spectful, or unreasonable) the text comment We identified four characteristics that make is, using a machine learning (ML) model that sensitive elements undetectable by the existwas trained by people’s rating of internet ing algorithms. comments. Challenge 1. Dificulties in Locating Fea
With the rapid improvement of Com- tures from Images of the Current Placeputer Vision (CV) technologies with ma- ment. Thingiverse automatically generates chine learning, several image datasets (e.g., rendered images of the 3D model when a 3D NudeNet Classifier dataset[15]) and moder- ifle is uploaded, and this is used as a represenation APIs enable developers to apply these tative image if the designer does not provide ready-to-use mechanisms to their applica- any photos of real 3D prints. In many cases, tions. For example, Microsoft Azure Con- these files are placed in the best orientation tent Moderator [1] classifies adult images that guarantees print-success in FDM (Fused into several categories, such as explicitly Deposition Modeling) printers, aligning the sexual in nature, sexually suggestive, or design to minimize overhangs. As the pregory. DeepAI’s Nudity Detection API [2] view is taken in a fixed angle, so it might not enables automatic detection of adult images be in a perfect angle that shows the main part and adult videos. Amazon Rekognition con- of the model thoroughly (e.g., Fig 1(a)). It hintent moderation [3] detects inappropriate or ders the existing image-based APIs from acofensive features in images and provides curate detection of sensitivity in the preview detected labels and prediction probabilities. images, because sensitive parts might not be However, many of-the-shelf services and visible.
APIs are often obscured, because it is hard for users to expect that the models are trained Challenge 2. Support Structure that Ocwith fair ground-truths that can ofer reliable cludes the Features. Following the model results to various stakeholders with diferent alignment strategy of FDM printing, designcultural or social backgrounds without any ers often include a custom support structure biases, which we will discuss more in a de- to prevent overhangs and to avoid printing tailed way in the following section. failures and deterring surface textures with (a) Rotated model (b) Support structure (c) Texture on surface (d) Divided into parts auto-generated supports from slicers (i.e., 3D model compiler) such as Cura [16]. These special structures easily occlude the design’s significant features (e.g., Fig 1(b)). Since the model is partly or completely occluded, the existing CV techniques barely detect sensi- 3.1. Transparency: Black Box tivity of the design. that Lacks Explanation
Challenge 3. Texture and Colors. Current 3D printing technologies enable users to use various print settings and other postprocessing techniques. Accordingly, the printed model may present unique appearances compared to general real-world entities. Often the model is single-colored and can have a unique texture such as linear lines on the surface (e.g., Fig 1(c)) due to the nature of 3D printing mechanisms of accumulating materials layer-by-layer, which might let the existing CV algorithms overlook the features.
Challenge 4. Models Separated into Parts for Printing. As one common 3D printing strategy to minimize printing failures from a complex 3D designs such as a human body, many designers divide their models into several parts to ease the printing process, and let users post-assemble as shown in Fig 1(d). In this case, it is hard for the existing CV techniques to get the whole assembled model, resulting in a failure to recognize its sensitivity.
Content moderation has long been controversial due to its non-transparent and secretive process [17], resulting from lacking explanations for community members about how the algorithm works. To meet the growing demands for transparent and accountable moderation practice as well as to elevate public trust, recently, popular social media platforms have begun to dedicate their eforts to make their moderation process more obvious and candid [17, 18, 19, 20]. As a reasonable starting point, those services provided detailed terms and policies (e.g., Facebook’s Community Standards [21]) describing the bounds of acceptable behaviors on the platform [17]. In 2018, as a collective efort, researchers and practitioners proposed the Santa Clara Principles on Transparency and Accountability in Content Moderation (SCP) [22]. SCP suggests one requirement that social media platforms should provide detailed guidance to the members about which content and behaviors are discouraged, including examples of permissible and impermissible content, as well as an explanation of how tificial intelligence (AI) in content moderaautomated tools are used across each cate- tion, it has long been in the black box [23], gory of content. It also recommends for con- thus not understandable for users due to the tent moderators to give users a rationale for complexity of the ML model. To address the content removal to assure about what hap- issue of the uninterpretable model that hinpens behind the content moderation. ders the users from understanding how it
Making the moderation process transpar- works, researchers shed lights on the blind ent and explainable is crucial to the success spot by studying various techniques to make of the community [23], in order not only to the model explainable (e.g., [29, 30, 31]). Exmaintain its current scale but also to invite plainability has been on the rise to be an new users, because it may afect users’ sub- efective way of enhancing transparency of sequent behaviors. For example, given no ex- ML models [32]. In order to secure explainplanation about the content removal, users ability, the system must enable stakeholders are less likely to upload new posts in the fu- to understand the high-level concepts of the ture or leave the community, because they model, the reasoning used by the model, and may believe that their content was treated the model’s resulting behavior [33]. For exunfairly thus get frustrated owing to an ab- ample, as shown in the Fairness, Accountsence of communication [24]. Reddit [25], ability, and Transparency (FAT) model, supwhich is one of the most popular social me- porting users to know which variables are dia, has equipped volunteer-based modera- important in the prediction and how they tion schemes resulting in the removal of al- will be combined is one powerful way to enmost one fifth of all posts every day [26] able them to understand and finally trust the due to violation of their community policy decision made by the model [34]. [27] (e.g., Rule 4: Do not post or encourage the posting of sexual or suggestive con- 3.2. Fairness: Implicit Bias and tent involving minors.) or individual rules of Inclusivity Issues the subreddits (i.e., subcommunity of Reddit that has a specific individual topic) ac- People often overlook fairness of the modcording to their own objectives (e.g., One of eration algorithm and tend to believe that the rules in 3D printing subreddit: “Any de- the systems automatically make unbiased device/design/instructions which are intended cisions [35]. In fact, the human adjudicainjure people or damage property will be re- tion of user-generated content has been ocmoved.”). Users being aware of community curred in secret and for relatively low wages guidelines or receiving explanations for con- by unidentified moderators [36]. In some tent removal are more likely to perceive that platforms, users are even unable to know the the removal was fair [24] and showcase more presence of moderators or who they are [37], positive behaviors in the future. As many so- and thus it is hard for them to know what cial platforms including 3D open communi- potential bias, owing to diferent reasoning ties such as Thingiverse highly rely on vol- processes, has been injected into the moderauntary posting of the user-created content tion procedure. For example, there have been [28], the role of a transparent system in con- worldwide actions that strongly criticize the tent moderation becomes more significant in sexualization of women’s bodies without inmaintaining the communities themselves. clusive inference (e.g., ‘My Breasts Are Not
Even if many existing social media plat- Obscene’ protest by the global feminist group forms have their full gears to implement ar- Femen [38] to denounce a museum’s censorship of nudity.). Similarly, Facebook’s auto- Work) by replacing their thumbnail images matic turning down of postings and selfies with the black warning images. It is a sethat include women’s topless photo by tag- cretive process because there are no clear raging them as Sexual/Porn ignited ‘My Body tionale or explanations ofered to users beis not Porn’ movement [39, 40]. The difer- hind this process. Therefore, users cannot exent points of view in perceiving and reason- pect whether Thingiverse operates based on ing towards the same piece of work makes an unbiased and fair set of rules. it yet hard to decide the absolute sensitiv- While the steep acceleration of increments ity. It is nearly impossible that the sole group of 3D models [43] is making automatic detecof users represent all, therefore, it is difi- tion of sensitive 3D content imperative, modcult for users to expect a ground-truth in the erating 3D content also faces fairness issues decision-making process, and trust the result and users are sufering from lacking explawhile believing experts made the final deci- nations. We need to take our account into sions based on thoughtful consideration with various stakeholders’ points of view that afan unbiased rationale. fect their decision on potentially sensitive 3D
Subsequently, many studies (e.g., [41, 42]) content, as well as further discussions to mithave explored potential risks of algorithmic igate bias and discrimination of the algorithdecision-making that are potentially biased mic decision-making system. Here we proand discriminatory to a certain group of peo- pose an explainable human-in-the-loop 3D ple such as underrepresented groups of gen- content moderation system to enable varider, race, disability. Classifier has been one ous users who have distinct rules to particcommon approach in content moderation, ipate in calibrating algorithmic decisions to but developing a perfectly fair set of classi- decrease bias or discrimination of the algoifers in content moderation is complex com- rithm itself. Although we focus on specific pared to those in common recommendation issues in shared 3D content online, our proor ranking systems, as classifiers tend to in- posed pipeline generally applies to advancevitably embed a preference to the certain ing a semi-automatic process toward an exgroup over others to decide whether the con- plainable and fair content moderation for all. tent is ofensive or not [17].
Through a text feature based classification, sensitivity with fairness is employing the huwe identified there are three main cate- man workforce with ample experiences in gories of sensitive 3D content: (1) sex- observing and perceiving with various perual/suggestive, (2) dangerous weaponry, and spectives. We suggest a human-in-the-loop (3) drug/smoke. Due to the capability of un- pipeline, based on the idea of incremental limited replication and reproduction in 3D learning [44] that the human workforce can printing, unawareness of these 3D contents collaborate with an intelligent system, concould be crucial. We noticed that Thingiverse currently classifying data input and annotate limits access to some of sensitive things that features with the explanation for the deciare currently labeled as NSFW (Not Safe for sion. 4.1. Building an Inclusive vide filtered cases for humans to support Moderation Process a decision-making process [24], if we wellecho diverse perspectives in understanding Making decisions on the sensitivity of a 3D contents. In our proposal of the human-inmodel can be subjective due to various fac- the-loop pipeline (Fig 2(a)), an input image tors such as cultural diferences, the nature dataset of 3D models will be used for the of the community, and the purpose of navi- initial model training, then the result will gating 3D models. To reflect diferent angles be reviewed by multiple human moderators in discerning the nature and intention of con- step by step. We trained the model with tents, we need to deliberate various interpre- 1,077 things that are already labeled as NSFW tations taken from various groups of people. by Thingiverse and 1,077 randomly selected For example, there are lots of 3D printable non-NSFW things. All input images are simreplicas of artistic statues or Greek sculptures ply categorized as NSFW or not, with no anthat are reconstructed by 3D scanning of the notation for specific image features to prooriginal in the museums [45]. Speculative vide the reasoning. Human moderators reK-12 teachers designing their STEAM edu- cruited from various groups of people now cation curriculum using 3D models are not review the classification results whether they likely to want any NSFW designs revealed agree. They are asked to annotate image segto their search results. On the other hand, ments using a bounding box where they rethere are many activists and artists who may ferred to make the final decision with the catwant to investigate the limitless potential of egory. At the same time, they provide the the technology, sharing a 3D scanned copy rough level of how much the part afected of the naked body of herself [46] or digitiz- the entire sensitivity and a written rationale ing nude sculptures available in the museum for the decision. These features will enhance to make the intellectual assets accessible to the data quality so to be used to fine-tune everyone, etc. The nude sculpture has been the model with the weighted score, thus the one popular form of artistic creation in his- model becomes able to recognize previously tory, and it is not simple to stigmatize these unknown sensitive models based on the simiworks as ‘sensitive’. Everyone has their own larity and now can explain sensitive features. right to ‘leave the memory of self’ in a dig- When two diferent groups of people with ital form. Forcing to adapt a preset thresh- diferent standards do not agree on the same old of sensitivity and filter these wide array model’s classification results, the model uses of user-created contents could unfairly treat their decision, annotated features, and levone’s creative freedom. As the extent that els of sensitivity to diferentiate the extent of various stakeholders perceive the sensitivity perceived sensitivity and reflect to the difercould be distinct, our objective is to design ent threshold. For example, one moderator an inclusive process in accepting and adopt- thinks that the model is sensitive while the ing the sensitivity. other does not, the model will have a higher threshold in categorizing the content. Dif4.2. Solution 1: ferent decisions on the same model finally Human-in-the-loop with could be brought to the table for further disAugmented Learning cussion if needed, for example, to regulate policy guidelines, or used as search criteria Automated content moderation could help for other community users who have similar review of a vast amount of data and pro- goals in viewing and unlocking analogous 3D (a) Human-in-the-loop pipeline (b) User interface mockup contents. To summarize, one iteration con- sexual activity, etc.). We currently refer to tains the following steps: a two-level hierarchical taxonomy of Ama1. The pre-trained model presents predic- zon Rekognition to label categories of inaption results. propriate or ofensive content.
Case 2. Sensitive Parts Ignored by the 2. The human moderator can enter dis- Algorithm Another possible case is that the agreement/agreement with the results specific feature in the image that the moderand annotate sensitive parts with a ator perceives as sensitive is missing in the sensitivity level and a decision ratio- detection results. In this case, human modnale. erators can label that part and provide ratio3. The annotated image is used to fine- nales using enter the level of sensitivity field tune the model. from 1 (slightly sensitive) to 5 (highly sensi4. If the decision for the image is dif- tive), how each specific part afects the entire ferent from other moderators, annota- sensitivity of the model. tions and sensitivity levels are used to Case 3. False Negative It is also possible set the diferent threshold. that some parts detected by the model are We elaborate more on feedback from the not sensitive for the moderator due to the moderators by showing three possible sce- higher tolerance to sensitivity. The modernarios: (1) the moderator’s agreement with ator can either submit the disagreement or the prediction results, (2) sensitive parts not provide more detailed feedback by excluding detected, and (3) false-classification of insen- specific results. sitive features sensitive. Diferent degrees of sensitivity perception Case 1. Agreement with the Predic- from various stakeholders can reflect distinct tion Result In case that the moderators points of view, which may manifest fairness agree with the decision, they can either fi- in algorithmic moderation through multiple nalize it or reject the classification, by se- iterations of this process. In our interface for lecting provided top-level categories (e.g., the end-users that assists searching 3D desexual/suggestive, weaponry, drug/smoke) signs, we let users set their desired threshold. and second-level categories (e.g., under sex- For those who might find it dificult to decide ual/suggestive, explicit nudity, adult toys, a threshold that perfectly fits their need, we show several random example images that relies on the users’ voluntary action given have detected sensitive labels with the corre- no oficial guidelines, resulting in a lack of sponding threshold. This pipeline also helps awareness that the users must be granted the obtain the explainable moderation algorithm. consent to upload possibly privacy-invasive Our model can help users understand the ra- contents at the time of posting those contionales of the model by locating detected tent in public spaces regardless of the comfeatures/prediction probabilities in the image mercial purpose. Without explicit consent, and providing written descriptions that the the content is very likely to be auto-filtered moderators entered for data classification. by Thingiverse, which decreases fairness by hampering artistic/creative freedom. To iron 4.3. Solution 2: New Metadata out a better content-sharing environment in Design to Avoid the these open communities, redesigning of metadata must be considered and adapted
Auto-Filtering by system admins that invoke responsible Another potential problem in open 3D com- actions. For example, providing a checkmunities is copyright or privacy-invasive box that asks “If the design is made of 3D contents that are immediately marked as scanned human subject, I got an agreement NSFW by Thingiverse indicating they are from the subject” can inform previously uninappropriate. Currently, Thingiverse lacks aware users about the need for permission to notification and explanation for content re- post potentially privacy-breaching contents. moval, while a majority of them might in- Including the subject’s consent can also provade copyrights. Its obscurity results in a tect creative freedom from auto-filtering, by negative impact on the user’s future behav- adding that the content is not breaching iors. For example, creators are frustrated at copyright or privacy and can be shared in the un-notified removal of their content thus the public spaces. In addition, it can enable decided to quit their membership (e.g., [47]), users to understand that an absence of conwhich might not happen if they saw an in- sent could be the reason for filtering. formative alert when they post the content.
Along with advanced 3D scanning technolo- 5. Conclusion gies [48], many creators are actively sharing 3D scanned models (e.g., As of December 2020, Thingiverse has 1150 things that tagged with ‘3D_scan’ and 308 things with the tag ‘3D_scanning’). With arising concerns over possible privacy invasion in sensitive 3D designs, what caught our attention is 3D scanned replicas of human bodies. Many of them do not include an explicit description of whether the creator received the consent from the subject (e.g., [49, 50]). Some designers quoted the subject’s permission, for example, one creator describes that the subject, Nova, has agreed to share her scanned body on Thingiverse [51]. Still, this process As an inclusive process to develop transparent and fair moderation procedure in 3D printing communities, our study proposes to build an explainable human-in-the-loop pipeline. We aim to employ diverse group of human moderators to collect their rationales, which can be used to enhance the model’s incremental learning. Our objective is not to censor 3D content but to build a pleasant 3D printing community for all, by safeguarding search as well as guaranteeing creative freedom, through the pipeline and new metadata design that has potential to minimize issues related with privacy or copyright. [20] M. MacCarthy, Transparency require- [29] B. Letham, C. Rudin, T. H. McCormick, ments for digital social media plat- D. Madigan, et al., Interpretable clasforms: Recommendations for policy sifiers using rules and bayesian analymakers and industry, Transatlantic sis: Building a better stroke prediction Working Group (2020). model, The Annals of Applied Statistics [21] Facebook, Community stan- 9 (2015) 1350–1371.
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