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The Golden Circle: Creating Socio-technical Alignment in Content Moderation

This paper outlines a conceptual framework titled The Golden Circle that describes the roles of actors at individual, organizational, and societal levels, and their dynamics in the content moderation ecosystem. Centering harm reduction and context moderation, it argues that the ML community must attend to multimodal content moderation solutions, align their work with their organizations' goals and values, and pay attention to the ever changing social contexts in which their sociotechnical systems are embedded. This is done by accounting for the why, how, and what of content moderation from a sociological and technical lens.


Introduction
Content moderation has become an increasingly challenging sociotechnical issue as sociolinguistic norms change at an ever-increasing rate and more people than engage online. While social scientists have focused on social issues of content moderation like poor labor conditions (Roberts, 2019) (Sablosky, 2021), technologists have mainly focused on developing and perfecting AI solutions to remove harmful content from the information ecosystem (Vijayaraghavan et al., 2019). Moving beyond the dichotomy of the technical vs. the social, we develop the Golden Circle Framework (GCF), an interdisciplinary approach to tackle the content moderation problem, which takes into account both individual ML researchers, developers' positions, and the collective action of organizations and other society actors.

The Golden Circle
A growing body of literature has pointed out that one should move beyond content moderation to "context-moderation" and "harm-reduction" (Caplan, 2018). The GCF, an adaptation of the Golden Circle by Simon Sinek (2009), provides a road map for evaluating content moderation harms and organizing responses to mitigate them on an individual, organizational, and societal level keeping in mind both the technical and sociological interventions. The central tenet of the GCF is that these vectors of effort (having both direction and magnitude as per the notion in Physics) need to be aligned to make content moderation efforts effective. Figure 1. The Golden Circle Framework. The inner most layer represents the "Why" that all actors agree on. The second layer is the "How." The outer most layer represents the "What." The motivating "Why" of content moderation is maintaining social cohesion and trust that underpins a fair and healthy democracy. Mistrust incurs steep social costs by creating sharp societal division in areas like healthcare (Sylvia Chou et al., 2020) and politics (Hameleers & van der Meer, 2020) and is likely in the long-run unsustainable in a democratic society. Yet, scholarship on content moderation has identified numerous harms to groups and social institutions when usergenerated content is not sufficiently moderated (Freelon et al., 2020;Schradie, 2019). The following harms must be reduced to preserve a healthy information ecosystem that supports social trust and cohesion.
(a) Radicalization: the availability of radical, extremist, violent and otherwise objectionable content online may lead to the radicalization of political or cultural views, whether through organized radicalization efforts such as ISIS recruitment networks or less organized methods like the spread of misinformation and disinformation (e.g. QAnon conspiracy theories) (Zuckerman & Gessen, 2019;Lewis, 2018). Radicalization and incite-arXiv:2202.13500v1 [cs.SI] 28 Feb 2022 ment to hatred or violence result from harmful content not being detected before it is posted or not being taken down after it has been posted.
(b) Polarization: the "echo chamber" effect results in users seeing more and more content that they are already more prone to engage with, over time resulting in highly homogeneous content consumption (Bail et al., 2018). Polarized political worldviews result in not only high levels of mistrust of the "other" but also different versions of the truth, or different epistemologies (Freelon et al., 2020). No longer able to agree on basic facts and a common reality, the public suffers polarization that makes democratic participation, deliberation, and decision-making difficult. Polarization results not necessarily from harmful content not being removed, but the patterns of information that are systematically served to some and not others (Liao & Fu, 2014).
(c) Online harassment: online bullying, harassment, and discrimination, often of marginalized groups or individuals based on race, gender, sexual orientation, disability, religion, or ethnicity, whether targeted at specific individuals or entire groups such as Rohingya Muslims (Siddiquee, 2020), results in exclusion, marginalization, and in the most extreme cases physical violence, and even genocide. This virtual (Patton et al., 2014) and physical violence results from harmful content not being detected before it is posted or not being taken down after it has been posted.
(d) Attention control: online content has been shown to be addictive by design. Internet "influencers" produce content that could go "viral" to get more followers; companies post "click-bait" to increase ad revenue (Wu, 2017). Ubiquitous social media content production and consumption result in increased feelings of isolation, decreasing ability to focus, feelings of a sense of loss of self-control, and the opportunity cost of not engaging in more healthy or productive activities (Sujarwoto et al., 2019). Attention deficit results from the sheer volume of content production and the commercial incentives to generate attention-grabbing and low-quality content (volume over quality).
(e) Labor: moderating online content incurs emotional and psychological costs on low-paid wage workers, who often reside in the developing world, to screen objectionable content (Roberts, 2019).
(f) Environmental: Automated content moderation systems which utilize large language models require a sprawling infrastructure that uses up natural resources and vast amounts of energy to operate (Bender et al., 2021). They incur environmental costs that must be taken into account when deciding how to regulate content production and moderation.
Why have these well documented harms not been adequately addressed? Our framework shows that even though everyone may agree on the "Why" (healthy information ecosystem), the "How" and the "What" are not aligned. In other words, though few would disagree with the need to maintain social trust and a healthy information ecosystem, current solutions to content moderation are not aligned with how individuals and organizations operate, especially when it comes to their incentives.
Our framework provides a road map for realigning content moderation solutions to the motivating "Why" while respecting the fundamental "How" of individuals and organizations that are largely immutable. The "How" for ML developers is to seek job rewards like promotions and benefits. Yet organizations don't incentivize harm reduction through improve content moderation, so sufficient technical improvements are not innovated. There is a feeback loop from organization to individual. Similarly, the "How" of organizations is to maximize profit while avoiding negative public opinion. Yet society, through civic and legislative action, has not demanded sufficient organizational change.
There is a feedback loop from society to organization. Each stakeholder level feedback enables the other and must be aligned. Then, and only then, can technical and sociological solutions -the "What" -be successfully implemented.
We propose this framework not as a single, one-time "solution" to content moderation but rather as a way to better align the vectors to drive toward outcomes that reduce harm, acknowledging that eliminating all harm completely is unrealistic. Let's turn now to the "What" -proposed solutions to reduce content moderation harms.

Individual
What can those who build algorithms that power social media experiences do about it? ML developers are uniquely positioned to offer technical solutions to sociotechnical problems, so their improvements are only a piece of the puzzle, but let us consider some proposals.
To reduce the amount of harmful content circulating in the information ecosystem, we need to improve the accuracy of models in identifying extremist, violent, or otherwise objectionable content, whether text, image, video or more and more some combination of each. The difficulties lie in algorithms understanding cultural references and symbols that are unspecified or implicit. To understand this nuance, models need to be better able to capture the cultural context within which meaning is socially produced (Caplan, 2018). For example, Gao & Huang (2017)  tic regression and neural net hate speech detection models by introducing contextual features like screen name, full comment thread, and the news article associated with each thread. They enlarged the object of analysis by including culturally-relevant context as features in the model. Improved model accuracy will also have the spillover effect of minimizing as much as possible the necessity for human review of objectionable content. The unfair toll on human reviewers has been well documented (Roberts, 2019).
Other machine learning innovations can have positive indirect effects on content moderation like attempts at reducing the echo chamber effect. Recommendation algorithms need to be redesigned to optimize for a healthy plurality of content (Sheth et al., 2011) rather than the spiral of homogeneous content that is the product of algorithms that optimize chiefly for views and clicks. Similarly, these algorithms need to consider features that can help reduce the addictiveness of content, focusing less on virality more on quality content (Del Olmo & Gaudioso, 2008). This can also help reduce the incentives to serve customers more and more radical content instead privileging more balanced and higher quality content. Finally, the environmental impacts of running large language models used in content production and moderation have been well documented. Algorithmic efficiency up and down the whole production chain must be improved or new solutions introduced to reduce environmental burden (Lacoste et al., 2019).
Recognizing that ML developers have a bias for technical solutions, next we need to expand the context within which models get built. This requires examining the organizational structures of social media platforms.

Organization
How can more research like what discussed above and additional innovations be encouraged and incentivized within social platform organizations? Currently there is a misalignment between organizational goals and culture, incentivizing ML teams to build bigger and better language models (Chelba et al., 2012) that are better at targeting content to users, not necessarily reducing harmful content or otherwise improving content quality. Unfortunately, there is limited available research on the kinds of organizational processes, tools or cultures that are most practicable and conducive to more ethical algorithmic solutions (Baucus & Beck-Dudley, 2005). Some innovative social scientific research, however, is moving in that direction. Moss & Metcalf (2020) published a report on "ethics owners," those in tech orga-nizations assigned to address ethical concerns, champion ethical causes, designs, development, and deployment of technology from within the tech industry. Central to our argument, they find that the "personal ethics of the ethics owner do not always align with those of the corporation" and that organization practices and business models "have implications for how ethics owners approach their work." Our framework proposes several possibilities by which social media platforms can align content moderation practices to more ethical outcomes while still maintaining profitability. These include increasing resources for ethical AI work and diversifying the AI workforce. An extensive body of research shows that more diverse teams build better products, increase sales revenue, leading to better organizational outcomes (Page, 2008;Muller et al., 2019;Harrington, 2010;Jehn et al., 1999;Herring, 2009). Investing in these areas should not cut into the bottom line. Second, organizations need to build interdisciplinary teams composed of technical and non-technical roles so that social implications of poor content moderation can be brought to the fore. This does not require hiring external personnel, but rather expanding the kind of experts that have a seat at the table. Third, organizations must restructure ML developer incentives to reward the development of algorithms that put human well-being at the center. This can include alternative ways of measuring employee progress or impact by de-emphasizing speed and quantity and instead focusing on harm reduction, alignment with the latest social scientific research, and customer satisfaction. Fourth, organizations should explore business models that more fairly distributes platform benefits. This could include compensation for content generators or data subjects while incentivizing quality content (Wohn, 2019). Technology companies routinely update, tweak, or "pivot," and sometimes completely invent new business models (Shestakofsky, 2017;Ravenelle, 2019;Griesbach et al., 2019). Just because the current advertisingbased business model of social media platforms is the most widespread and profitable does not mean it is the only viable model possible. Investing in business model innovation will create incentives for model developers to think more creatively about how machine learning can be employed for different purposes than it currently is. Finally, organizations must foster a culture of transparency, integrity, and accountability where critical research and criticism of company policy is not only tolerated but encouraged. ML researchers and developers cannot propose innovative solutions without first being able to point to what is not working.

Society
Broadening our lens even further, and to force the kind of realignments mentioned above, here we propose some actions for legislators, NGOs and academe.
First, legislators and regulators must limit the outsized power social media platforms currently hold on content moderation (Langvardt, 2017). Much like there are federallymandated standards for content production for other industries like film, television, journalism and other media, so too we need democratically-elected representatives to set the standard for social media. Second, legislators must consider laws that incentivize platforms to do better content moderation by developing legal and regulatory instruments to hold them accountable for social harms when they refuse or fail to comply (Gorwa et al., 2020). Third, NGOs and academics must continue to scrutinize content moderation practices, centering especially those that are most harmed by failures, to help organizations see problems they might not otherwise see, while holding them accountable for fixing them. Due to corporate secrecy and legal protections around proprietary information and trade secrets, content moderation practices continue to remain black boxes, even if some efforts at transparency reports have improved that marginally in recent years. This is why support from legislators is needed to make it easier for civil society organizations and academics to study and evaluate content moderation practices by mandating certain levels of transparency in the name of public interest. Finally, citizens, social rights organizations, journalists, academics, and policymakers all need to participate in a robust dialogue on the role of social media in society and negotiate a set of common principles that can guide policy action. The technical and complex nature of machine learning pose challenges for this kind of informed conversation, but as the field of machine learning becomes more and more mainstream so too its broader understanding will improve.

Conclusion
To conclude, efforts like ours and many others (Sloane & Moss, 2019;Lindgren & Holmström, 2020) at bridging the gap between machine learning experts and social science researchers (and the broader public) strive to better align the practices of all stakeholders (ML developers, organizations and society) for the purpose of generating improved social outcomes. Our framework points out current day misalignments so that we can address these gaps in a more organized manner. This is needed because the harms of an unhealthy information ecosystem are real, as evidenced by recent events like the attack on the U.S. Capitol. Our framework uses the case study of content moderation as one field within which ML solutions are applied. But it has significance for other sociotechnical AI-based problems as ML becomes applied to more and more aspects of our social lives.