Content Moderation by LLM: From Accuracy to Legitimacy
Tao Huang

FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
    
    
    Brian Hyeongseok Kim
    

    University of Southern California 

    Los Angeles, USA

    Jingbo Wang
    

    Purdue University

    West Lafayette, USA

    Chao Wang
    

    University of Southern California 

    Los Angeles, USA

    Received 16 July 2024; accepted 04 September 2024
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§ ABSTRACT 
One trending application of LLM (large language model) is to use it for content moderation in online platforms. Most current studies on this application have focused on the metric of accuracy – the extent to which LLM makes correct decisions about content. This article argues that accuracy is insufficient and misleading, because it fails to grasp the distinction between easy cases and hard cases as well as the inevitable trade-offs in achieving higher accuracy. Closer examination reveals that content moderation is a constitutive part of platform governance, the key of which is to gain and enhance legitimacy. Instead of making moderation decisions correct, the chief goal of LLM is to make them legitimate. In this regard, this article proposes a paradigm shift from the single benchmark of accuracy towards a legitimacy-based framework of evaluating the performance of LLM moderators. The framework suggests that for easy cases, the key is to ensure accuracy, speed and transparency, while for hard cases, what matters is reasoned justification and user participation. Examined under this framework, LLM's real potential in moderation is not accuracy improvement. Rather, LLM can better contribute in four other aspects: to conduct screening of hard cases from easy cases, to provide quality explanations for moderation decisions, to assist human reviewers in getting more contextual information, and to facilitate user participation in a more interactive way. Using normative theories from law and social sciences to critically assess the new technological application, this article seeks to redefine LLM's role in content moderation and redirect relevant research in this field.









§ INTRODUCTION

LLM (large language model) can be used in a wide range of scenarios. One emergent area of its application is content moderation. Within a span of less than two years, such topic has generated considerable scholarly discussion (see <cit.>; <cit.>; <cit.>; <cit.>; <cit.>; <cit.>; <cit.>; <cit.>; <cit.>; <cit.>). Most studies so far have deemed LLM promising in this application field and the technology has been hailed as “the greatest change to the dynamics of content moderation in at least a decade” (<cit.>).

Even though researchers have explored LLM's contribution in different types or aspects of moderation tasks, one conclusion shared by most of them is that the new tool, through appropriate design and use, could be more accurate in detecting and classifying violating content than its predecessors. In other words, the existing scholarship on LLM moderation has centered upon the metric of accuracy – the extent to which LLM makes correct decisions of moderation. LLM's superiority in accuracy derives from its technical structure and pretraining process, which enable them to better understand complex contexts as well as to adapt to changing circumstances. Such accuracy bonus drives the advocacy of many researchers that the new technique should be encouraged for wider use in managing content (<cit.>).

This article argues, however, that accuracy is far from enough in situating and evaluating LLM’s role in content moderation. On the one hand, it is impossible to make all decisions right. Content moderation involves the inevitable tradeoffs and balances among various rights and interests. On global platforms, in particular, people from different cultural backgrounds may reasonably differ on how the balance or tradeoff should be made. Like judicial adjudication by courts, moderation by online platforms also face hard cases. On the other hand, accuracy is not the only goal of a moderation system. Content moderation is not an isolated process, but a constitutive component of platforms’ governance scheme. The central purpose of such scheme is to provide legitimacy to the private exercise of governance power, which, unlike the state governance, has neither constitutional authorization nor democratic approval. To justify their power of delimiting people’s fundamental freedoms online, platforms must maintain a governance scheme that helps them gain legitimacy. As a vital component of governance, content moderation must also serve this central goal, rather than the narrow focus of improving accuracy.

This article proposes a paradigm shift from the accuracy discourse to a more comprehensive analytical framework based on legitimacy. The centrality of legitimacy derives from dual needs of protecting people’s fundamental rights and curbing platforms’ formidable power. The concept of legitimacy examines how the practice of moderation could be normatively justified and accepted. Answering the inquiry of how the new technology of LLM can contribute to content moderation is possible only after it has been positioned into the holistic structure of platform governance, the central concern of which is legitimacy. That being said, the right question to be asked is not whether and how LLM can help reach more accurate moderation decisions, but whether and how it can enhance the legitimacy of the moderation and governance system of online platforms.

Legitimacy is a broad concept that consists of far more factors than accuracy, such as transparency, speed, rationale of decisions and procedural fairness(<cit.>; <cit.>). It contains both substantive and procedural aspects. The framework proposed by this article makes a distinction between easy cases and hard cases, and uses different legitimacy metrics to measure the moderation of the two categories. It does so for two reasons. First, people hold different expectations toward easy cases and hard cases, which, as a result, entail different normative implications for the legitimacy of the governance scheme. Second, the numbers and moderation costs of the two categories are disparate, so it is advisable for them to be managed by different governance strategies.

For the easy cases, which are those containing clear answers and little controversies, moderating them should be accurate, fast, and transparent. When straightforward answers are available, the moderation system should deliver those answers in an efficient and open manner, ensuring the health and vividity of the online discourse. For hard cases, which involve complex fact contexts or difficult value compromises, requiring fast and correct decisions is unrealistic, since there may hardly be any agreement on what decision is correct. Rather, the legitimacy of deciding hard cases depends upon the substantive quality of the explanation (reason-giving) for the decisions, as well the procedural justice of the decision process (especially the participation of users). Easy cases fit with the administrative model which based on statistical and probabilistic management (<cit.>), while hard cases should be dealt by the juridical approach, using procedures similar to those of the courts. 

Examining LLM under this framework reveals that the most promising strength of this tool is not to increase the accuracy of moderation, but to conduct pre-screening of hard cases from easy cases, to provide quality explanations for moderation decisions, to assist human reviewers in getting more contextual information, and to facilitate user participation in a more interactive way. LLM’s accuracy advantage is significantly discounted by its increased latency and cost in moderating easy cases, as well as the diminished relevance of accuracy in hard cases. Instead, LLM can play valuable roles in various other aspects that promote the legitimacy of platform governance. 

The accuracy discourse is parochial, misleading, and counter-productive in the sense that it has led substantial academic and industrial resources to an enterprise that produces little return. Achieving higher accuracy is not the best application scenario here; researchers and developers interested in LLM moderation should consider reorienting their focus of efforts. Locating LLM within the legitimacy framework helps such redirection by aligning the technological project of progressive improvement with the socio-legal concerns of normativity and justification. It allows us to rethink LLM’s real potential in content moderation and platform governance, as well as what improvements can be made to further realize its potential.

This article aims to make three contributions. First, after reviewing the current literature on LLM’s capability in achieving accurate moderation and the possible reasons behind (parts 2 and 3), I argue that accuracy is a poor metric for measuring the performance of LLM and guiding its design in content moderation tasks (part 4). Second, I propose a framework that focuses on legitimacy, under which various substantive and procedural metrics will be used for assessing the performance of moderation (part 5). Third, positioning LLM under the framework reveals both promises and challenges (part 6), and they point directions for future work of research in technical and legal fields (part 7).

It should be noted that this article deals with moderation by LLM, not moderation of LLM. The former refers to the use of LLM as tools for moderating online communities and platforms; while the latter is the moderation of content produced or generated by LLM. Moderation of LLM is a component of governing LLM to prevent it from producing harmful messages. This area also attracts much scholarly attention (see <cit.>) but should not be confused with the research issue of this article.




§ THE ACCURACY DISCOURSE



LLM moderation is gaining momentum. A simple Google Scholar search shows that more than a thousand papers have discussed this topic in the past two years.[The search of keywords “LLM” and “content moderation” generated 1220 results in Google Scholar. The search was conducted on Aug 5, 2024.] Researchers and developers have growing interest on using this technology to moderate content on digital platforms and communities. Most existing studies on LLM moderation have focused on the metric of accuracy. Accuracy refers to the capability of making correct decisions. It is usually measured by the percentage of correct decisions (true positives plus true negatives) among the total number of cases.

There are also metrics that are akin to accuracy, such as recall and precision: some may stress the ability of identifying true positives and others may stress the ability of identifying true negatives (<cit.>, 21). And tradeoffs are sometimes inevitable between the two sides (<cit.>, 45). But the general goal of all AI moderation tools is to reduce the two types of errors and increase the ratio of correct ones. This article uses accuracy in this broad sense, referring to the capability of making correct (true) decisions and avoiding erroneous (false) ones.

Among the 1220 results in Google Scholar after searching “LLM” and “content moderation”, 844 of them contain “accuracy”, constituting roughly two thirds. Generally, studies to date reported positive appraisals regarding LLM’s accuracy performance in moderation tasks, such as identifying and classifying content. (<cit.>, 2) found that in most cases, ChatGPT’s zero-shot accuracy is higher than that of MTurk (crowd-workers of annotation on Amazon). (<cit.>, 9) attested that “LLMs can perform on par or better than traditional ML models in cyberbullying detection. (<cit.>) revealed that LLMs exhibit a high degree of accuracy in detecting fake news. (<cit.>) tested the fine-tuned LLMs’ performance in fact-checking and discovered that “LLMs can reliably assess the relationships between social media posts and verified claims, offering performance comparable to human evaluations”. Similar work of testing LLM’s ability in fact-checking has been done by (<cit.>). What’s more, (<cit.>) has used chain-of-thought reasoning to prompt the LLM and observed a superior rate of accuracy in detecting new waves of online hatred.

Using LLM to moderate online content not only attracts much scholarly attention, it also generated heated discussion in the industry. One of the most successful LLM providers, OpenAI, has enthusiastically touted its product, GPT4, for conducting content moderation tasks (<cit.>; <cit.>). The chief reason for OpenAI’s enthusiasm is, again, GPT4’s impressive performance in accuracy. The major evidence that OpenAI offered to support its claim is a chart that lists GPT4’s F1 scores in various categories of content, as compared to human moderators (<cit.>). As a reminder, F1 score is calculated as the harmonic mean of precision and recall – both are mathematical metrics that measure the level of accuracy (<cit.>).

To be sure, not all authors are optimistic. Some also expressed caution and concern for the wide use of LLMs in content moderation, at least at the current stage (<cit.>). One concern, for example, is the uneven error rate of LLMs: one study has found that GPT-3.5 “is much more likely to create a false negative (86.9% of all errors) than a false positive (13.1% of all errors)” (<cit.>, 4). Another limit is LLMs’ capability beyond the moderation of bad content. Most of the current studies on the accuracy of LLM moderation focused on detecting and classifying bad content. But sometimes a community may need to enforce rules not on bad content, but about benign content that is not suitable to be posted in the specific community. One study shows that state-of-the-art LLMs may not be sufficiently capable of accurately moderating non-harmful content in these niched communities (<cit.>).

The list could go on and on, and new publications are burgeoning, exploring the potentials and limits of LLM’s accuracy performance in moderation. This reflects the dominance of using accuracy as the benchmark in the scholarship of LLM moderation. This article uses “accuracy discourse” to describe this academic inclination. Accuracy is surely important, and it is natural or intuitive to assess LLM’s performance in moderation through the metric of accuracy. In any case, everyone expects moderation decisions to be correct, and if the new technology can increase the ratio of correct decisions, then it would also be intuitive to advocate for further adoption of such technology in this domain.

However, the accuracy discourse is parochial and misleading. It attracts most scholarly attention and resources in this field, some of which should have been spent on other equally, if not more, important issues. This article will argue that accuracy is far from enough in evaluating the content moderation practices, and the most promising area for LLM’s application in moderation is not accuracy enhancement. This conclusion may sound surprising to some researchers, but it is strongly supported by the arguments below. Moving our interests and endeavors beyond the accuracy discourse would significantly enrich our research agenda and make our enterprise more productive. Before a full critique of the accuracy discourse in part 4, the next part investigates another important question about the accuracy discourse: what makes LLM different from its predecessors in moderating content, and what unique features of LLM may explain its accuracy superiority. The inquiry of next part helps unearth the rationale behind the optimistic tone of the accuracy discourse, as well as lays the foundation for the later parts of this article by providing necessary technical backgrounds of the new tool.




§ THE UNIQUE FEATURES OF LLM MODERATION


AI moderation tools have been widely used by online platforms and communities to manage content, as human moderation is impossible to scale and costly to operate. Several methods have been adopted for various circumstances, such as hashing (<cit.>, 4) and blacklisting (<cit.>, 41). Currently, the most widely used is the technique of machine learning (ML). Since LLM is one sub-category of ML, this article uses “traditional ML” to refer to the technique of ML before the emergence of LLM. To know what differences LLM could bring, we should first take a glimpse at the basic characteristics of the traditional ML method and its limits.

Traditional ML moderation contains several steps: 1) developers build a ML model with nodes or “neurons”, the connections among which have been assigned with different weights; 2) human reviewers or annotators read and annotate the content of a dataset; 3) the annotated dataset is then used to train the ML model, enabling the model to adjust its weights to fit with the pattern of the dataset (<cit.>); 4) after training, the model is utilized to review the real-life content (<cit.>, 38). This process can be continuous and iterative. If the ML model’s decisions were further reviewed by human moderators, the human decisions can be “fed” into the learning process of the model, facilitating its improvement (<cit.>, <cit.>). The key condition for this technique is human supervision, which determines the quality of the annotated datasets as well as the learning process (<cit.>, 2).

The traditional ML method for content moderation has several limitations: First, it heavily relies on manual annotation of the training dataset. Such practice generates substantial costs for subsidizing the labor (<cit.>; <cit.>, 2) and also introduces biases into the process (<cit.>). In addition, different human annotators may not agree on the decision of a piece of content, especially for highly subjective categories such as hate speech (<cit.>, 35, 10; <cit.>, 8; <cit.>, 79). Second, the traditional method lacks flexibility and adaptability. A model trained by one dataset can hardly perform equally well in other datasets, represented by different cultures, languages, or contexts (<cit.>, 78; <cit.>, 626). Traditional ML models cannot be easily adapted to changing circumstances because doing so would require re-annotation of the dataset and re-training of the model (<cit.>, 562). Such feature of domain specificity significantly limits the scope of application of these models (<cit.>, 8). Third, the traditional ML approach lacks explainability and transparency. Even though the ML method may perform well in identifying rule-violations of online content, it cannot provide explanations to the users (<cit.>, 84). Transparency of the process is also missing: we hardly know any meaningful details about how the decisions have been made by the algorithmic black-box (<cit.>, 38; <cit.>, 2).

Recently, LLM has emerged as a trending technology that shows impressive performance in multiple task settings. One area of its application is content moderation. Technically speaking, the major difference between traditional ML models and LLMs is that the former is target-trained with specific datasets, while the latter is pre-trained with massive corpus of online data. LLMs are based on deep learning (DL) techniques which can “extract pertinent features from textual data, eliminating the requirement of manual feature engineering – a typical practice in conventional ML approaches” (<cit.>, 34). In other words, LLMs are self-supervised (<cit.>, 1959), as they can automatically learn implicit language patterns from the immense body of online materials without human annotation and supervision. This key difference brings two distinctive features of LLM in the task of content moderation, which may explain its promising performance in the accuracy metric.

First, LLM has the potential of better understanding contexts and nuances. There are two reasons for this. One reason is the distinctive training process of. The pretraining of LLM by large corpus of data exposes the models to a wide range of content from diverse sources (<cit.>, 16-17). The sources may contain billions of web documents, potentially covering most areas of knowledge that has been stored online (<cit.>, 1961). Such scale and diversity enable LLM to generalize across different domains, and to develop comprehensive understanding of common language use (<cit.>, 17, 22). One particular reason why LLM can better appreciate contexts than previous ML model is that the former have seen more variations of expressions, idiomatic phrases, and emerging language trends. Broader exposure empowers LLM with the capacity of capturing more contextual expressions, such as irony, sentiment, and sarcasm. Knowledge at such scope is extremely helpful for moderating expressions online, which are complex, nuanced, and constantly changing. By contrast, traditional ML methods rely on targeted datasets that are much narrower in scope.

Another reason why LLM excels in contextual reasoning lies in the transformer architecture used by most dominant LLMs (including the popular GPT) (<cit.>). Such architecture uses the mechanism of self-attention, which takes the whole content of user input into consideration, even when the distance between two words is remote (<cit.>, 1964). In other words, it weighs the importance of each word in a sentence in relation to every other word, rather than the neighboring words only (<cit.>). Such capability facilitates the model to capture long-range dependencies and contextual relationships within user input, rather than grasping meanings through its fragmented parts. This characteristic partly explains LLM’s superiority in holistic and contextual understanding.

Second, LLM is more adaptable and flexible than traditional ML models. LLM does not rely on manual labeling of datasets; rather, their pretraining is unsupervised (<cit.>, 1). Because the decision logic is decoupled from the model, LLM can quickly adapt to changes of policy and context without re-annotating the data and re-training the model (<cit.>, 562). Through pretraining, LLM already acquires contextual knowledge and understanding across a diverse range of areas; when new context emerges, LLMs can be adjusted to fit with the new context through prompting or fine-tuning (<cit.>, 3; <cit.>, 2). The deprivation of the continuous need of updating quality datasets and annotating them manually renders LLM especially capable of dealing with emergent crisis or events. For example, in dealing with new waves of hatred online, platforms can easily adapt the LLM “by only updating the newly identified hate targets and derogatory terms in the prompts rather than the model” (<cit.>, 2).

These are the main promising aspects of LLM moderation that leads to the optimistic tone in the accuracy discourse. To be sure, LLM also contains risks and limitations, such as the high cost, the problem of hallucination, and the inevitable bias inherent in their pretraining.[See infra part 6.1] But the revolutionary technical features make the tool an eligible candidate of the next-generation technology for moderation. Unfortunately, however, current discussion and research focus almost exclusively on exploring LLM’s capacity of achieving high(er) accuracy in moderation. Such parochial vision limits the endeavor of discovering the full potential of LLM in the governance of online communities. Accuracy is surely an important metric, but as the following part shows, it is not the only one that matters; and in some cases, it may even be misleading and counter-productive to pursue accuracy as the top priority. Indeed, the technical features of LLM, such as its broad data exposure, generative functionality, and contextual adaptability, can contribute to many other aspects of moderation apart from accuracy.




§ WHY ACCURACY IS NOT ENOUGH


The accuracy discourse is parochial and misleading. Taking accuracy as our primary or even exclusive metric of evaluating and designing LLM moderation tools misunderstands the function of accuracy and its position in the governance system.

There are four cases against the accuracy discourse. First, it is impossible to achieve perfect accuracy in practice, and trying to do so is dangerous. Second, accuracy has both individual and systematic aspects, but the current discourse stresses the former while ignoring the latter. Third, the discourse failed to recognize the distinction between easy cases and hard cases, which should be taken differently in platform governance. And fourth, focusing exclusively on the substantive result of decisions overlooks other aspects of content moderation that are also critical for governing the platforms. This part will illustrate these four points in turn. 



 §.§ Can we make all decisions right?


The accuracy discourse, as reflected in most studies about LLM moderation so far, holds that the goal of designing and incorporating LLM into moderation is to achieve high percentage of correct decisions – the higher, the better. If accuracy is the benchmark of evaluation, then the ideal state is that all moderation decisions will be made correctly; in other words, LLM can perfectly tell whether an online post violates the community rules. That is the aspiration that LLM developers should pursue.

Such discourse has one assumption behind: that there exists, and we can identify, an objective or consensually endorsed standard of determining right decisions (true positives and true negatives) from errors (false positives and false negatives). That assumption, however, does not stand. Determining whether certain content violates the platform’s rules involves probing the facts, identifying the relevant rules, and making a choice in front of value conflicts. It requires delicate assessment and shrewd judgment. Without objective standard, the only metric for measuring algorithmic decisionmaking is the decision of humans. Actually, human moderators’ classification has been used as the “ground truth” that trains and evaluates AI tools (<cit.>, 46). Facebook has hired a separate reviewing panel (containing three reviewers) to evaluate the correctness of first-instance human moderators (<cit.>, 13). This is also the case for experiments that test LLM’s capability of moderation. During (<cit.>, 3)’s experiment, for example, they employed experienced human reviewers to resolve disputing cases.

The problem with this approach is that human moderators sometimes differ in moderating cases. “By definition, content moderation is always going to rely on judgment calls, and many of the judgment calls will end up in gray areas where lots of people’s opinions may differ greatly” <cit.>. One salient issue, as noted by many researchers in this field, is the intercoder disagreement, referring to the instance that different moderators cannot reach consensus on a case (<cit.>). In such circumstances, whose decision should be counted as “ground truth” or the standard of accuracy?

The existence of disagreement reveals that we cannot make every case right, because not every case can be decided by humans in a consensual and non-controversial way. In fact, “[n]o legal system guarantees a right to an accurate decision. Enforcement of speech rules has never been perfect, online or off” (<cit.>, 799). People reasonably disagree on many issues, especially issues regarding moral, religious, and other value judgments (<cit.>). Observation on legal adjudications shows that determinacy and consensus are not always available, otherwise there would be much less lawyers, appeals, and dissenting judicial opinions. Just take a look at the free speech cases decided by the U.S. Supreme Court: within the past 20 years, the court decided 43 cases on free speech; 26 of them contain dissenting opinions (about two thirds!) and 7 of them are decided with a divisive vote of 5:4.[The statistics here is up to the date of June 18, 2024.] The prevalent disagreement on legal cases illustrates that consensus on accuracy is an illusion.

The goal of perfect accuracy is not only impossible to achieve, it is also undesirable. Perfect accuracy means there will be, and must necessarily be, a single, uniform, and perfect standard for measuring accuracy. If there is not, we must design one. But doing so would greatly suppress the space of value contestation by imposing one set of values upon the whole platform. Consider the fact that most online platforms are global, with users coming from divergent cultural backgrounds (<cit.>). Striving for uniform and definite applications of rules may serve the goal of formal consistency, but sacrifices other vital principles such as value pluralism, open disagreement, and epistemic humility. This is unacceptable for governing global and open platforms.




 §.§ Two levels of accuracy


Current studies on LLM moderation uses accuracy in the individual case level, aiming for making correct decisions in each and every individual case. This perspective ignores that accuracy can also be measured in the holistic or system level. System accuracy is not the aggregate of the accuracy scores of individual cases. Rather, it refers to the general performance of the whole moderation system, including metrics like consistency, predictability, and the fairness of error distribution across different groups of users, different categories of content, and different periods of time. Two insights can be gained by taking the system level of accuracy into account.

First, the perspective from system level further corroborates why perfect accuracy is not a worthwhile goal. One can reasonably anticipate that the moderation of one case to be accurate. But things will get much harder if the number of cases arises to millions or billions, and the time for moderating each case is very limited (sometimes only seconds for a post) (<cit.>, 549). Moderation also takes costs, no matter who does the job – human or AI. The factors of time, cost, and scale at the system level renders moderation a managerial enterprise that must constantly make tradeoffs under limited resources. Errors are to be managed by considering costs, not to be avoided at all costs. That’s why people have widely accepted that it is “unrealistic to expect rules to be applied correctly in every case” (<cit.>, 766), and that perfect accuracy is “unaffordable” and “intolerably costly” (<cit.>, 185-6).

Second, viewed through the system lens, accuracy is not only a numerical metric, measuring the performance of moderation by percentages. Rather, accuracy is also a distributional metric. If, as the foregoing analysis suggests, errors are inevitable and perfect accuracy is illusory, then how to manage accuracy and how to distribute errors become crucial (<cit.>, 791). The right questions worth examination should include, for example, “[w]ho suffers from false positives and false negatives? ... [w]hich types of errors are known and tolerated, [and] how is risk distributed” (<cit.>). Accuracy in the individual case level is a poor indicator for these issues, since it ignores the distributional effect. Even though the total accuracy is sufficiently high, say 99.99%, it would still be worrisome if the tiny percentage of errors (0.01%) are mostly suffered by LGBTQ groups or racial minorities. To date, few works (one example is <cit.>) on the accuracy of LLM moderation has discussed the distribution of accuracy across different demographic groups. Future studies should pay more effort on this aspect. 



 §.§ Easy cases vs. hard cases


The foregoing analysis revealed several tensions within the practice of content moderation: that accuracy is an important metric for moderation but it is also parochial and misleading; that good governance requires making correct decisions but the scale and resource limit render such task extremely challenging; that content moderation is individually similar to judicial adjudication but systematically more akin to administrative management. Evelyn Douek is right that perfect accuracy is impossible and “system thinking” is necessary for approaching content moderation (<cit.>). But she neglects the fact that accuracy and other metrics can coexist, so does the individualistic/judicial thinking and the systematic/administrative thinking (<cit.>, 343). This article argues that the two modes can be smoothly combined in the platform governance. One crucial premise is the distinction between easy cases and hard cases. For easy cases, the systematic/administrative thinking will apply, and the measuring standard is the correct application of rules to cases in a time-sensitive and transparent way. For hard cases, the individualistic/judicial approach is more appropriate, the goal of which is less of reaching correct outcomes, than affording justification and participation to the stakeholders.

Distinguishing between easy cases and hard cases is reasonable and even necessary for three reasons. First, such distinction already exists in platforms’ content moderation practice. Meta has reported that “[i]n most cases, identification is a simple matter. The post either clearly violates our policies or it doesn’t. Other times, identification is more difficult” (<cit.>). This claim not only acknowledges the distinction between easy and hard cases but also discloses the fact that easy cases count for a dominant majority of all moderation cases, while hard cases are relatively few. Platforms constantly draw the line between easy and hard cases when they divide labor between AI moderators and human moderators (<cit.>, 12, 14). For example, Meta uses AI tools to “identify and remove a large amount of violating content—often, before anyone sees it”, and when “technology misses something or needs more input”, human reviewers will intervene to make their judgment calls (<cit.>). Such practice reveals that platforms must make, and already have made the strategic choice of treating different cases differently.

Second, easy cases and hard cases have also been distinguished in legal systems, for similar reasons (see generally <cit.>; <cit.>). In most instances, we get highly determinate answers regarding what the law prescribes (<cit.>, 423): consider our behaviors adjusted according to the law, the quick and clear advice we receive from lawyers during consultation, and cases that are settled or finalized at trial level (<cit.>, 412-3; <cit.>, 597; <cit.>, 225). Sometimes the result to a case is unclear or contested so that value judgments are needed (<cit.>, 596): think about the cases that are appealed or those with dissenting opinions from the deciding judges. In some sense, the institutional design of litigation costs, hierarchies of courts, and judicial writing of dissents is made to distinguish between easy cases and hard cases,[Dissent is one signal of controversies among judges and can be used as an indicator of hard cases. However, the opposite is not true. A unanimous judicial decision without any dissenting opinions does not necessarily indicate that the case is easy. Such Unanimity may reflect the court’s strategic choice of speaking with one voice. Take Brown v. Board of Education. See (<cit.>, 408).] so that limited judicial resources could be reasonably distributed, and clear issues can be quickly resolved while difficult ones can be sufficiently debated and contested.

Third, easy cases and hard cases have different social impacts and different expectations from the users. For easy cases, answers are relatively clear and obvious; what users and the society expect is their resolution in a correct and convenient way. Substantive results are what matter the most, so accuracy should be the dominant metric here. Hard cases, by contrast, are controversial and contested. They typically involve complex facts or contentious value judgments, and people do not and may never have consensual answers to those cases. Should images of women breasts and nipples be prohibited (<cit.>)? If there are exceptions, what exceptions should there be? If child nudity is generally banned but can be allowed in some cases due to, e.g., newsworthiness or awareness raising, how should the boundaries be drawn (<cit.>)? What about hate speech like Holocaust denial (<cit.>)? When reasonable people differ on the substantive results, what matters most is not who wins and who loses, but how the result has been reached. Participation and procedure may be more important than the contested correctness in these cases (<cit.>, 320-1).

The existence and salience of hard cases illustrate why accuracy should not be the overarching metric that applies across the board. Hard cases defy the clear standard of telling right decisions from wrong ones. For those cases, accuracy is not only impossible, but also irrelevant, for in front of disagreements, conformity with one party’s vision of justice should not be used to judge the fairness of the resolution.

The rights discourse mandates that all actions infringing basic rights (such as the right to free speech) should be justified in a way as a fair trial. However, the scale of content moderation on digital platforms dictates that it is impractical to subject every moderation case to a judicial-style adjudication bound by complex procedural safeguards. In other words, “there is a tension between the evaluation of AI tools from a statistical perspective (how well are they performing overall) and their evaluation on a case-by-case basis, which is the predominant mode of evaluation from a fundamental rights perspective” (<cit.>, 10). That’s why Masnick has famously declared that “content moderation at scale is impossible to do well” <cit.>. “To do well” here means to make the decisions correct from the juridical perspective and in a case-by-case manner. If the juridical approach cannot be applied at scale, shall we abandon it entirely? Or, is it savvy to discard the rights discourse and juridical approach entirely and replace them with probabilistic and systematic management (<cit.>)?

The answer is no, because people still expect moderation by platforms to be done correctly and still use the rights discourse to evaluate the practice – how the rights have been infringed by the moderation decision and whether such infringement is fair. Such discourse filled with the headline moderation cases in news coverage. Sure, these cases are significantly outnumbered by the easy and invisible cases and cannot reflect the system design behind the scene (<cit.>, 529-30, 559). But the public attention these cases draw demonstrates their predominant influence. These cases greatly shape the public’s view of platforms’ moderation and the users’ level of acceptance of their governance power. Unlike state institutions, private platforms cannot build legitimacy of their power upon ex ante democratic authorization. Rather, their legitimacy relies mostly upon ex post public acceptance. Justification and contestation of contentious and influential cases is one crucial way for garnering public acceptance and gaining legitimacy. Even though the hard cases only constitute a tip of the giant moderation iceberg and cannot bring overall accountability to the moderation system (<cit.>, 530), these “high-profile content moderation controversies” act as paradigms that shape the public discourse and opinions about platform governance.

As both easy cases and hard cases matter for the platforms’ governance scheme, this article argues for combining the systematic management approach and the individualistic juridical approach. Due to the large scale and relatively non-controversial feature of easy cases, it is appropriate to subject them to systematic and probabilistic management. By contrast, hard cases are contested in nature and few in numbers, making them the proper objects of judicial and adjudicatory processes.




 §.§ Content moderation is a part of governance


The accuracy discourse views content moderation as an isolated process, a process in which platform rules are mechanically applied to individual cases. Framed in this way, content moderation is like a syllogistic game. But content moderation is never simply the application of rules or the adjudication of disputes. Rather, it is, and must be taken as, a constitutive part of the governance system of platforms. Perceiving content moderation as a component of governance will highlight many more metrics other than accuracy. “Because substantive outcomes will always be fundamentally contestable and contested, the task is not to arrive at ‘right’ answers” (<cit.>, 820). Rather, the principal goal of content moderation is to enhance the legitimacy of platform governance.

Descriptively, the personnel, budget, and tools for content moderation are all internal components of the governance structure of platforms (<cit.>). Most platforms are private companies, which have no legal duty to moderate content generated by users. National laws such as the Section 230 of U.S. has granted them broad immunity against liability arising from the user-generated content.[47 U.S.C. § 230.] However, all platforms choose to moderate content voluntarily. The motive for doing so is mixed: preventing state regulations, promoting public image, making their products more profitable, etc. But one common concern underlying these different types of motives is legitimacy (<cit.>, 104) – the concern of how platforms’ exercise of power could be justified and recognized (<cit.>, 387; <cit.>, 68; <cit.>, 666).

Normatively, legitimacy should be the central concept in evaluating the practices of content moderation and platform governance. This is due to two, interrelated reasons: rights protection and power check. Moderation is the process of delineating the boundaries of free speech when it conflicts with other rights or interests. The exercise of many basic rights of people is dependent on the private platforms (<cit.>, 1668). Public and human rights law prescribes that any state intervention or infringement on fundamental rights must be appropriately justified to be legitimate. The stakes are not lower when the infringing body is a private company. These private platforms exert tremendous power of influencing people’s basic freedoms. As (<cit.>, 38) summarized, “the largest online platforms, such as Facebook and Google, exercise more power over our right to free expression than any court, king, or president ever has—in view of the very significant percentage of human discourse that occurs within the boundaries of these ‘walled gardens.’” The exercise of such power must be scrutinized with and measured by the lens of legitimacy. Actually, scholars have advocated for “digital constitutionalism” which aspires to ensure that platform powers are wielded legitimately (<cit.>, 2). The power of delimiting people’s fundamental rights must be legitimate for it to earn acceptance, obedience, and respect (<cit.>, 178).

As an essential and definitional component of online platforms (<cit.>, 21), content moderation must be viewed as a constitutive unit of governance, the goal of which is to legitimize the exercise of platforms’ power of defining people’s fundamental rights and shaping the contour of the online public square. The enterprise of legitimization entails increasing the public’s trust and acceptance of the power exercise. It includes, as the next part elaborates, both substantive and procedural aspects. Accuracy is not only insufficient but also misleading for measuring and evaluating LLM moderation, as it exhausts our attention that should have been paid to other metrics. It makes the discussion parochial and ineffective. By contrast, a more suitable and comprehensive framework of evaluating and guiding content moderation must take the broader concept of legitimacy as its governing principle. The next part introduces such a framework.




§ A LEGITIMACY-BASED FRAMEWORK FOR CONTENT MODERATION




 §.§ Introductory notes on the framework


This article is not the first endeavor that proposes a framework for normatively guiding the content moderation practice. For instance, (<cit.>, 96) has devised a framework on the basis of two metrics, risk and accuracy. This framework failed to notice that risk is not the only factor of distinguishing moderation cases and accuracy is merely one of the several elements that matter. (<cit.>, 1038) has compared the relative strengths and weaknesses of machine and human moderators, covering factors such as contextual adaptability, speed, and consistency. However, their work overlooks procedural legitimacy and the disparate implications of different cases. Based on the previous discussion, this part introduces a framework that distinguishes between easy and hard cases on the one hand, and covers both substantive and procedural aspects of legitimacy on the other.

Scholars have proposed various benchmarks or principles as indexes of legitimacy, such as transparency, democratic participation, and rule of law (<cit.>, 5; <cit.>, 2-4). Legitimacy has both substantive and procedural aspects (<cit.>, 379). Substantive legitimacy evaluates the content of decisions – whether they are correct, fair, or conforming to some high values or principles (<cit.>, 676-7). Another crucial component of legitimacy is procedural justice, which refers to the approach, manner, and process in which rules are enacted and enforced <cit.>. Justice Marshall has succinctly summarized the two aspects as “the prevention of unjustified or mistaken deprivations and the promotion of participation and dialogue by affected individuals”[Marshall v. Jerrico, Inc., 446 U.S. 238, 242 (1980)] . This is also the case for content moderation. Users care about both the outcome of their cases and how they have been treated in the process. The public holds serious concern over how the platforms’ moderation system performs as well as to what extent it remains accountable.

Few existing studies of LLM moderation have drawn a distinction between easy cases and hard cases. One exception is (<cit.>, 2), which argues that LLM can automate moderation decisions on clear cases, so that human moderators can focus on borderline cases that require more expertise. This article further elaborates on this distinction and suggests different metrics for evaluating the legitimacy of the two groups separately. Before introducing the metrics, one practical issues needs to be addressed: how to separate them – how to screen the hard cases from the easy ones? This might be a challenging task since “it is not clear how to know in advance which areas are safely bulk and which are more controversial, as the landscape of controversy changes over time” (<cit.>, 3). Meta has described the hard cases as involving content that is “severe, viral, nuanced, novel and complex” (<cit.>). But these descriptions are too abstract to be manageable. Like the similar distinction in the legal system, distinguishing easy and hard cases in moderation context is not a clear-cut practice (<cit.>, 88; <cit.>, 600). Instead of offering a bright line, I argue that for cases to be hard, the following conditions should be taken into account:

1) Complexity of facts and contexts. Disagreement about fact is one type of causes that makes a case hard (<cit.>, 93). Lack of clear and comprehensive understanding of the contextual facts of a case may inhibit judges or moderators from reaching a clear answer. The Facebook Oversight Board, for example, stated in the famous moderation decision about the suspension of Trump’s account, that because Facebook refused to provide detailed information about how the platform’s technical design amplified Trump’s inciteful posts, the Board could not fully assess whether the removal of account is a necessary measure in that case (<cit.>). One pertinent factor here is the category of content. Some content generally requires less contextual knowledge, such as spam, child porn, and IP infringing materials. Other categories of content may be highly contextual and culturally dependent, such as hate speech, which is more likely to make a case hard.

2) The vagueness of platform rules. Sometimes the moderation rules may be too vague to dictate a clear or singular result. Ambiguity of rules is more complicated than non-existence of rules. When there are no rules applicable for a case, we can resort to the principle that those which are not prohibited are permitted (<cit.>, 484-5). But when a rule is so ambiguous in its applicability or that it dictates multiple reasonable, but potentially conflicting, results (<cit.>, 94), moderators have to use other tools, sometimes judgment calls, to pick from those conflicting options.

3) The textual meaning of a rule contradicts with its underlying purpose or value, or violates some established principles of morals (<cit.>, 415-6; Hutchison 1989, 560). Sometimes the application of a rule may lead to unconventional or controversial results (<cit.>, 488). This requires the decisionmaker to make a choice between adhering to the textual mandate and resorting to some external values or morals.

4) Plurality of rules. This arises when there are multiple rules that are applicable to a case (<cit.>, 94; <cit.>, 415). The Meta Oversight Board, for example, has three sources of rules for its judgment: Meta’s Community Standards, Meta’s values, and the International Human Rights Law (IHRL) (see <cit.>). Sometimes these different sources of rules may be in conflict. This happens in one case, in which the Board held that Facebook’s moderation decision conforms with its community standards, but the decision should be overturned because it violates the Values of Facebook as well as the IHRL (<cit.>).

In reality, the division between easy and hard cases should not be a fixed line, but a fluid one, subject to changing circumstances. For example, during political and social crises, such as ethnic conflicts, national elections, and public health events, different response strategies can be used to address these crises (<cit.>). One strategy is to lower the threshold of hard cases, letting more cases to be classified as “hard” and then be elevated to human or expert review. Actually, as will be elaborated by part 6.2, the threshold can be set with the help of LLM by setting a confidence score to the model.

Below, I introduce the framework based on the core benchmark of legitimacy and the distinction between easy and hard cases. As a general framework of evaluating content moderation, it is not limited for applying to LLMs. Rather, the framework can be used to measure the performance of different moderators (e.g. traditional ML, LLM, ordinary human moderator, and expert moderator) according to the legitimacy criterion. Identifying the suitable role of each type of moderators depends on their relative strengths and weaknesses as compared to other moderators.




 §.§ Easy cases: making correct, fast, and transparent decisions


Easy cases are those with clear answers: either the post has violated platform rules or not, and the type of violation is obvious. Moderating this type of cases is the routine job of moderators. From the legitimacy perspective, moderating easy cases must serve two goals: for users, cases should be resolved in a correct and timely manner; for platforms, the communicative space must be regulated fairly, efficiently, and openly. These are the goals that are not only achievable but also indispensable for gaining trust for the moderation practice. From these goals we can derive three legitimacy metrics for moderating easy cases:

1) Accuracy. Users expect cases to be decided accurately, and platforms also anticipate right moderation in order to gain public trust and to build amicable communities. Accuracy should be measured at both individual-case level and system level. The former refers to the aggregate percentage of correctly decided cases, while the latter requires the distribution of accuracy – or, viewed in the opposite angle, errors – to be fair across different groups of people, categories of content, and periods of time.

2) Speed. One salient difference between online moderation and offline speech adjudication is the velocity and virality of communications in the former context. In the online world, it is more appropriate to say “justice delayed is justice denied”. For cases with obvious answers of right or wrong, providing the right answers in a timely manner means delivering justice to the users. Speed is also crucial for the moderation system as a whole, since maintaining the health of the platform requires fast removal of harmful content.

3) Transparency. Transparency refers to the extent to which the moderation process is visible to the public. This metric ensures that the users are informed about the moderation decisions as well as the internal moderation process (<cit.>, 1527). As an old principle of justice, transparency can be imposed on the individual case level and the system level. The Santa Clara Principles, for instance, urge the platforms to not only disclose the numbers (statistics) of the moderation system but also provide users with individual notices about the moderation decisions.[The Santa Clara Principles, available at https://santaclaraprinciples.org/]

At both levels, the transparency requirement can be either loose or strict, subject to considerations of practical needs. At the individual level, the notice to relevant users can be a brief note that states which piece of content has been dealt with and which provision of rules has been violated; the notice can also be much more detailed, including information about why the content violated the specific rule, how the violation has been identified, whether the moderation has been done by AI or human, what is the technical makeup of the AI and what is the decision process of the human moderator (<cit.>, 1536, 1539). In the system level, likewise, the statistics disclosed can be concise or comprehensive. The degree of transparency requirement should take into account the operational cost and potential burden that may impose on the platforms. One particular concern is the effect on competition, since the costs of fulfilling transparency mandates may be unbearable for small and startup platforms, positioning them with greater disadvantages in the market.



 §.§ Hard cases: enabling justification and participation


Hard cases defy clear and consensual answers. For the users, especially the losing party, to accept and respect the decisions of hard cases, the key is not the substantive result, but the process of reaching that result. On the one hand, the contentious decisions must be justified to be accepted; on the other hand, users and stakeholders must be fairly treated in the moderation process.

1) Justification. The more contentious the case, the more it needs to be justified. Jurists like Hart and Dworkin shared the view that for hard cases, adjudicators need to justify their decisions (<cit.>, 80). Feldman argued, in his proposal of establishing the Facebook Oversight Board, that “[when] there is no magic-bullet solution to balancing competing values … [t]he advantage enjoyed by real-life constitutional courts is that they openly address difficult cases, and so derive credit and legitimacy from being principled” (<cit.>, 102). The metric of justification mandates that explanations be provided to the relevant parties as well as the public. This echoes with the principle of reason-giving in public law (<cit.>, 75). Explanation here is not the same thing as the metric of transparency in easy cases: while the latter refers to the disclosure of moderation details in a systematic, statistical, and holistic manner, the former evaluates the quality of explanation in individual cases. Explanation has been recognized as an important factor of legitimacy: for example, empirical findings show that “users who did receive some sort of explanation from moderators regarding their removal were less likely to have posts removed in the future” (<cit.>, 71). In addition, reading explanations of moderation decisions is an educational opportunity for users to learn and internalize the community norms <cit.>.

To be sure, explanations should not aim for gaining wide approval on the merit of the decisions. But refraining from the ambition of achieving consensus on substantive results does not mean that all the substantive reasonings are equally persuasive and equally acceptable. Not all explanations can be qualified as justifications (<cit.>, 1288). Justificatory effect entails that the explanations provided must reach certain level of substantive quality. For example, how the decisions address the facts and rules of the case as well as the context of the controversy, how the decisions respond to users’ concern and the public expectation, and how the decisions approach pressing issues like the borrowing of human rights norms into private moderation context, are all important aspects for measuring the substantive legitimacy of the explanations.

2) Participation. If justification (reason-giving) earns substantive legitimacy for moderating hard case, then participation secures the procedural side of legitimacy. That participation is a central tenet of procedural justice is “as old as the law” (<cit.>, 308). Scholars found that people would deem algorithms more acceptable when they are more informed about how the algorithms work and afforded more control in their work (<cit.>, 4, 20). This corresponds with the general finding that “[a]ffected individuals are more likely to perceive a decisional system as legitimate ‘when they play a meaningful role in the process’” (<cit.>, 1548).

Importing principles from public and constitutional law, the participation metric prescribes that users should have a fair say in the moderation process: this includes, e.g., the right to be informed or noticed, the right to comment, and the right to appeal (<cit.>, 7-8; <cit.>, 391-2). The normative meaning for hard cases is not their perfect resolution, but the process of debating and deliberating them in a public way – that is crucial and even constitutive for an open society (Gillespie 2020, 3-4). The procedural safeguards ensure that hard cases could be contested in an accessible and inclusive manner. Of course, these procedural requirements are also a matter of degree, subject to practical limits of cost and other considerations.



The defining feature of this analytical framework is that it makes three kinds of distinctions: that among different types of cases, that among different categories of moderators, and that among different specific metrics. The last distinction is the result of making the first two distinctions. In this regard, to examine the performance of LLM under the framework, we need to ask the following questions: will LLM be used to moderate easy or hard cases, what is LLM’s strengths and limits on the relevant metrics, and compared to whom. 

 

§ LLM MODERATION UNDER THE FRAMEWORK


The main argument supporting the accuracy discourse and its sanguine tone can be summarized as this: because of LLM’s superior contextual understanding achieved during their extensive pretraining, it is capable of making more accurate judgments in moderation tasks, many of which involve contexts and nuances. This part will argue that accuracy is not LLM’s major field of contribution to content moderation. For easy cases, replacing traditional ML models with LLMs may bring some accuracy bonus, but LLMs also generate additional cost and latency; and as easy cases do not involve much contextual complexity, traditional ML models already perform reasonably well in this category. For hard cases, accuracy is not an important metric, since the standard of what is accurate becomes blurred here. Rather, what matters is the quality of justification for the decisions as well as the procedural justice offered by the decisionmaking process. 
 


 §.§ Reassessing LLM’s accuracy capability


Examining LLM under the framework proposed by this article and comparing its performance with other types of moderators, we can see that LLM’s superior capability in accuracy comes with significant limitations and costs.

First, even though LLM has achieved impressive improvement in accuracy, it cannot take the place of human experts in moderating hard cases. It is true that LLM’s exposure to large corpus of language equips it with more contextual knowledge and more proficiency in recognizing linguistic variations. This capability is a competitive advantage compared to the target-trained ML models, the knowledge of which is limited to the specific training datasets. And some studies indicated that LLM’s accuracy score is higher than the outsourced human moderators (<cit.>, 2). But LLM cannot, at least for the short run, reach the level of experts. According to OpenAI, LLM and ordinary human moderators (with light training) perform equally well in labeling accuracy; but both are outperformed by expert moderators that are well-trained (<cit.>). Expert moderators, with systematic training, sufficient decision time, and organizational and financial support from the platforms, are the best performers in accuracy – actually, expert labeling has commonly been used as the “ground truth” for determining what is accurate (<cit.>, 13). To be sure, it is impractical for experts to moderate easy cases due to scale and budgetary limit. But the quality of their reasoning and justification makes experts the best choice for moderating hard cases. That’s why Meta has delegated some of its hard cases to a board of experts (Oversight Board), the role of which can never been fully replaced by LLM.

Second, if LLM’s accuracy superiority cannot find its place at moderating hard cases, how about the easy cases? In the realm of easy cases, the currently major moderators are traditional AI tools and ordinary human reviewers. Even though their accuracy capability has been surpassed by LLM, platforms still have to be cautious of replacing them with LLM.

One reason is speed. LLM generally spends more time in generating decisions because of the larger scale of parameters they have. Ordinary human moderators are generally slower than machines but are quicker than expert moderators, as they spend about 10 to 30 seconds on average in moderating one piece of content (<cit.>, 32; <cit.>, 116). LLM moderation is quicker than human moderation, but takes much more time than its AI predecessors. It is reported that moderating one piece of content could take LLM a few seconds (<cit.>, 9). High latency makes it unlikely for LLM to fully replace the role of traditional ML models in moderating easy cases, where speed is a key parameter. To be sure, LLM can supplement traditional ML in some cases, and technical improvement may increase LLM’s speed in the future. But speed is a concern that should not be ignored, especially in real-time moderation tasks.

The second concern is cost. LLM moderation is a costly practice (<cit.>; <cit.>, 10). On the one hand, many LLMs, especially their API, are proprietary and charge fees for their uses. On the other hand, fine-tuning the models for specific scenarios introduces extra costs, including computational resources, preparation of the fine-tuning dataset, and the required expertise (<cit.>, 212). Researchers reported that for moderation of highly contextual content like hate speech, the quality of the fine-tuning dataset significantly affects LLM’s accuracy performance (<cit.>) – high-quality annotation of the dataset necessarily invites additional spending. Resource intensiveness poses a substantial obstacle for platforms, especially small ones, to put such technique in wide use. In fact, due to the law of diminishing returns, the small accuracy gain brought by LLMs may come at a price of substantial cost. That means, after a certain point, “the cost of reducing the marginal rate of error would become higher and higher... [and platforms would] ...invest enormous resources for an infinitesimal gain in accuracy” (<cit.>, 247).

Third, the buoyant mood toward LLM’s accuracy performance gains more vigilance in considering the new technology’s inherent limits. One limit is bias. Like all other ML models, LLM is not immune from biases. Bias can come from “online users who produced the pretraining data, feedback from crowdworkers during Reinforcement Learning from Human Feedback (RLHF) process and potentially, the decisions made by the model developers themselves” (<cit.>, 1). One particular concern is the majoritarian bias. The most salient difference between LLM and traditional ML models is that the former is based on pre-training within much larger datasets. Larger training datasets can provide more diverse knowledge and more contextual understanding to the models. But the drawback is also obvious. The more training data has been fed into the model, the more it will align with the knowledge, perception, and opinion of the mainstream. Taking what the majority holds as the “truth”, LLM “may reinforce majority views and further marginalise minority perspectives” (<cit.>, 23). Because the training datasets of LLM contain more materials on the dominant cultures, languages, and positions (<cit.>, 23-24), its application may contain the risk of entrenching the mainstream viewpoints and strengthening the existing power dynamics within the online discourse (<cit.>, 8056).

Another technical challenge for the state-of-art LLMs is the existence of hallucinations (<cit.>, 8056), which can cause inaccuracies and inconsistencies in moderation. Hallucinations are the non-sensical or fabricated answers produced by LLMs; they are not fully explainable: in many occasions, developers do not know why they emerge and how to prevent them (<cit.>). Even though LLM could moderate at a high level of accuracy in the aggregate, the inconsistencies and lack of explainability caused by hallucinations may hamper their performance in other metrics including system accuracy and transparency.

In addition, current studies on LLM moderation either test the accuracy of LLMs in detecting certain categories of speech (such as hate speech or misinformation), or prompt the LLMs with a particular rule of content. In both occasions, researchers give the LLMs a rule for a specific category of content, and then LLMs are asked to determine whether a post violates that rule. In reality, however, the rules of content in a platform are very complex, covering many kinds of speech; and the first thing LLMs must do is to determine the appropriate rule that is applicable to a piece of content. In other words, all the content rules (tens of thousand words at least, for a big platform[See “Facebook Community Standards”: https://transparency.meta.com/policies/community-standards/]) should be prompted as input to the LLMs. This may further exacerbate the latency of LLMs in moderation tasks. One study shows that once multiple policies have been prompted to LLMs, their accuracy of classification will decrease and the costs will increase (<cit.>, 9-10).

The above analysis suggests that LLM’s diminishing advantage in accuracy compared to expert moderators makes it not suitable for moderating hard cases. For easy cases, LLM does generate accuracy dividends for the moderation system, but advocacy for its application should meet with caution because of the increased latency and cost, as well as the inherent bias and hallucination in the models. If LLM should replace neither expert review in hard cases nor traditional AI and ordinary human moderators in easy cases, then their accuracy bonus would be much less useful than currently expected.




 §.§ LLMs’ real potential in content moderation


If accuracy is not the major field of LLM’s contribution, then what areas can LLM play a role in? Recognizing LLM’s limited use in accuracy does not dictate its dim prospect in content moderation. As this part will argue, LLM can make significant contributions to content moderation and platform governance in at least four aspects: to conduct screening of hard cases from easy cases, to provide quality explanations for moderation decisions, to assist human reviewers in getting more contextual information, and to facilitate user participation in a more interactive way.

1) Distinguishing easy and hard cases is crucial since they should be assigned with different resources and strategies. LLM can help the task of differentiation. One simple way is judging by disagreement. For example, human moderators and LLM can moderate the same content simultaneously; if the two disagree, then it is likely to be a hard case (<cit.>, 4). Another approach is to ask LLM to generate confidence scores for cases (<cit.>): if the confidence score is low, that means the model may not be certain about the result of the case – in this scenario, the case is probably hard. To facilitate LLM’s capacity on this task, we can fine-tune LLM with an annotated dataset that containing easy and hard cases. Due to costs consideration, however, platforms may not choose to screen all the cases through LLM. Rather, they can either use LLM to screen only those cases which have been appealed by users, or those which had already been marked as uncertain by traditional ML models. Using LLM as a second screener can supplement the first reviewer with more contextual knowledge; and instead of replacing all the work of traditional ML models, using the two tools in a collaborative way is also more financially sustainable.

Researchers found that LLM exhibits satisfactory performance in pre-filtering content, that is, to remove clearly non-violative content from the queue of moderation, as well as to escalate clearly violative content for human review (<cit.>, 11). LLM can also self-evaluate their answers by measuring the level of confidence (<cit.>, 3). These findings corroborate LLM’s technical capacity in distinguishing hard cases from easy ones. In practice, LLM’s confidence score can be adjusted according to changing contexts such as the need to address emergencies or crises – this is another testimony of LLM’s adaptability. LLM can also provide rationales for its screening. This is especially important for the influential headline cases, for explaining why they have been escalated for further review is necessary to address the public concerns.

2) As a type of generative AI, LLM owns one defining feature: the capacity of generating text content. Unlike traditional ML models which moderate content in an opaque way, LLM is iterative by design. Such feature, supported by its extensive training, makes it capable of providing high-quality explanations for moderation decisions, enhancing transparency of the process (<cit.>, 7-8). LLM can provide not only reasons of decisions to users but also possible ways of revising their posts to make them conforming with the content policies. Such informative process is dialogic and can even starts before the content has been made public (<cit.>). (<cit.>, 5) found that the explanations provided by LLM, though not identical to human reasoning, look quite convincing to users. In some occasions, LLM explanations can be more comprehensive than those written by human annotators (<cit.>, 4).

LLM’s strength of explainability can be utilized not only in their own moderation process, but also for helping justify the decisions made by other moderators, such as the traditional AI tools or the human reviewers. When researchers provide the LLM with moderation decisions and relevant content rules, it can generate quality explanations for the decisions (<cit.>). As has previously argued, it may not be economically feasible for LLM to directly moderate easy cases; but it can help AI and human moderators generate contextual explanations for their decisions if needed. This could save substantial time for other moderators, enhancing efficiency for the whole system.

3) LLM can also assist other types of moderators by providing more contextual information for their reference. Due to extensive pre-training, LLM’s biggest advantage lies in its “comprehensive grounding knowledge, strong language understanding, and logical reasoning capabilities” (<cit.>, 2). The ground knowledge, which has been fed into the LLM during pretraining by vast text corpora, enables the model to acquire basic understanding across different platforms, contexts, and communities. Such capability of making cross-domain generalizations makes LLM distinctive from traditional ML models, which rely on quality labeled data on specific platforms or contexts (<cit.>). Empowered by such comprehensive and diverse knowledge, LLM can be consulted in the moderation process concerning issues such as the factual background of a case, the cultural context, and the delicate value considerations. These types of information can then help the other moderators make more accurate and judicious decisions.

Even though it would be too expensive to totally replace the traditional AI tools with LLM in moderating easy cases, LLM can still play a role in the systematic management of platforms by assisting the traditional methods. For example, LLM can monitor the algorithmic moderation system by offering statistical insights such as toxicity score (<cit.>) as well as explanations on those metrics. LLM can also help the traditional AI methods by generating datasets for the training of the latter. This technique has been called data augmentation (<cit.>). Deploying such capability of LLM can produce synthetic data, reducing the burden of collecting and labeling data for training the ML models (<cit.>, 5). These are instances where LLM could boost the functioning of other moderators with its informational competence.

4) Putting transparency into the framework of governance, we can conclude that “[e]xplanation is not just about providing accurate information about how AI works, but fundamentally social and situated” (<cit.>, 102:18). Transparency is far more than the one-way disclosure of information: it is also a communicative process between the platform and the users (<cit.>, 1539). LLM can make significant contribution to improving such interactive design. In its interactive API, LLM could not only educate users about the rules and norms of the platform, but also hear feedback from users to improve the rulemaking and moderation practices. Users could pose their comments on whether the justificaiton of a decision is powerful enough and whether some other factors should be considered in making the decision (<cit.>, 3). Such interactive route enhances users’ control and agency in the process (<cit.>, 9-10). The dialogic feature of LLM makes it suitable for conducting this task.

The conversational capability of LLM can help solicit user participation and feedback in various occasions, such as the revision of content rules and the appeal of moderation decisions. Researchers have devised tools that incorporate user feedback into the LLM explanations, and such feedback can be then used to further improve the models (<cit.>). Empowering users to have their voices heard would greatly enhance the legitimacy of the moderation practice as well as the governance scheme as a whole.

From the above illustration on LLM’s potential in content moderation, two concluding remarks can be summarized. First, identifying the suitable role of LLM in content moderation requires us to put this tool within the whole system of platform governance and compare it with other types of moderators. Each type of moderator has its strengths and limits. Traditional AI methods are quick and cost-effective, but they can hardly grasp contextual nuances and provide meaningful explanations. Outsourced human moderators are reasonably efficient and perform fairly well in understanding contexts, but they “are poorly paid, inadequately trained, and traumatized by exposure to the worst that the internet has to offer” (<cit.>). Expert reviewers are the most expensive and most time-consuming, but they are superb in accuracy and reason-giving – so they are suitable for moderating hard cases, especially those with the most social impact. Comparing LLM’s strengths and weaknesses with those of other moderators is necessary for finding a suitable position of LLM moderation.

Second, in order to enhance legitimacy of platform governance under the framework, one crucial strategy is that different types of moderators should not only fulfill their own strengths, but also help and supplement other moderators. In other words, the four types of moderators should collaborate and help with each other in the governance scheme. For example, expert moderators could train the ordinary moderators in the moderation practices (<cit.>, 20). Likewise, LLM can provide help to other moderators in various aspects, such as supplementing more contextual information, generating more comprehensive explanation, and offering avenues for soliciting user input. Utilizing LLM for content moderation is not an isolated effort of excavating the new technique’s potential in its own regard, but a holistic enterprise that takes the relational structure of governance into account. 



§ TECHNICAL AND LEGAL IMPLICATIONS


Broadening our discourse on LLM moderation from accuracy to legitimacy is the first step. To better facilitate LLM’s contributary role in content moderation and platform governance, endeavors from both technical and legal/policy fields are needed. This part sketches some technical and legal issues that may arise from the paradigm shift. Due to limit of space, the discussion below is only introductory. Instead of offering firm conclusions or detailed suggestions, this part aims to point directions for future work.

On the technical side, there are two urgent issues that await researchers to explore. One is bias and arbitrariness, the other is latency and cost. Bias and arbitrariness are, to some extent, an inherent feature of ML models, because the probabilistic and statistical approach they rely upon necessarily contains randomness (<cit.>, 8, 17). Apart from the models themselves, arbitrariness and bias can also be introduced in pretraining and fine-tuning processes. The datasets for pretraining, though large and diverse, cannot be comprehensive enough to cover all knowledge. Developers must select and curate the datasets. Thus, the datasets may be more influenced by some social viewpoints than others, as well as reflect biases of the selectors. Besides, biases can be produced during fine-tuning because the process “might overgeneralize alleged patterns and…falsely associates those patterns with positive or negative labels” (<cit.>, 12).

One particular type of bias or arbitrariness is the phenomenon of predictive multiplicity, in which different ML models with similar accuracy performance may produce conflicting individual decisions (<cit.>). This is troublesome because from the legitimacy perspective, people expect decisions about their rights to be consistent and predictable. However, seemingly innocuous choices about the seed and parameters of a model may affect the performance of the model, generating different outcomes in individual cases (<cit.>, 2). What’s worse, researchers found that the arbitrary decisions are not only rampant in LLMs, but also “unequally distributed across different demographic groups” (<cit.>, 3), displaying significant ideological divide in their answers (<cit.>, 1-2). To address this issue, technical developers could further explore the relationship between the model selection and the statistical distribution of outcomes, as well as investigate better ways of mitigating biases and inconsistencies in LLM moderation. One direction is to make better use of prompt engineering to trigger more consistent decisions from the model (<cit.>, 15).

Another drawback of the state-of-art LLMs is their latency and cost. This not only bars LLM from wide adoption, but also creates disadvantages for small platforms. One way of reducing the costs of LLM moderation is to develop smaller models from the large ones: such method can reduce computation costs while retaining similar capabilities of the large model (<cit.>, 32). To be sure, the drawbacks of latency and cost come from the current status of the technology, and one may be confident for future progresses to overcome them (<cit.>). In such a fast-evolving field, “[w]hat may be a limitation today may be improved tomorrow” (<cit.>, 1944). In any case, lowering the level of expense and response time of LLM is definitely one of the top priorities for the technical field.

On the legal/policy side, at least three issues need further research. First, legal regulations must strike a balance between the needs of moderation by LLM and moderation of LLM. To mitigate the harmful content generated by LLM, moderation of their output is needed. One effective approach is to conduct curation during LLM’s pretraining process (<cit.>); another approach is to add safety filters in LLM’s API (<cit.>, 24). The curation and filtering are used to prevent malicious content to be fed into the model. But they would also compromise the model’s capability of conducting moderation tasks, because lack of exposure to harmful content makes LLM less effective in recognizing and classifying these types of content. That means, there is tension between promoting safety and preserving integrity of the training data (<cit.>). If laws impose strict requirement upon the health and safety of LLM, then its ability of moderation will likely decline. Lawmakers and policymakers must reach a delicate balance between the two competing goals.

Second, even though LLM exhibits impressive performance in explaining decisions, the explanations generated by them are not always reliable. Sometimes, LLM may be uncertain about its reasoning, expressing confusion like a human (<cit.>, 2). And like the previous technical tools, LLM also contains the risk of cloaking errors and biases with techno-objectivity. When LLM provides seemingly convincing but actually misleading explanations for its decisions (<cit.>, 3), such explanations cannot be counted as valid justification. To address this issue, regulators could consider either mandating right of users to contest LLM explanations or requiring more transparency on the development of the model to facilitate easier identification of false and misleading explanations.

Third, incorporating LLM into the system of platform governance demands corresponding regulatory and public oversight. Remember that LLM is itself a tool of power and a site for control. When it is used for governing online spaces, it becomes more than urgent to limit and oversee such formidable power. LLM contains bias, generates hallucinations, and is vulnerable to manipulations (<cit.>). How to ensure that the LLM moderator, as a component of platforms’ governance scheme, is accountable to the public ends and serving legitimate goals? This is a question that regulators should not wait to tackle with. Liability law is one tool, for it can provide strong incentives. The specific rules of LLM liability should take into account the specific application scenarios and their stakes on legitimacy. The aim is to incentivize LLM to be used in a responsible way that enhances, rather than threatens, the legitimacy of platform governance. Another tool is transparency rules. There are various choices that has to be made in LLM moderation: the selection of models, the selection of pretraining datasets, as well as the selection of finetuning datasets and finetuning method. Each choice has substantial impact. Disclosing meaningful details and rationales of those choices would facilitate public oversight on LLM moderation. 



§ CONCLUSION


Using LLM for content moderation is an exciting field to work on. Generative AI and LLM constitute one of the most revolutionary technical breakthroughs in recent years – and their applications have reshaped many industries. Realizing LLM’s full potential in moderation tasks depends upon locating this technology within the governance structure of online platforms and communities. The currently dominant discourse on LLM moderation focuses on accuracy – how to deploy the tool to increase the ratio of correct decisions. However, this article argues that accuracy is not the best area of contribution by LLM. LLM’s accuracy advantage as compared to traditional AI and ordinary human moderators has been largely offset by its weakness in cost and latency. Rather, LLM can make meaningful contributions in other aspects, such as distinguishing hard cases from easy ones and providing interactive channels for user participation. Moving from accuracy to legitimacy, we can get a clearer picture of LLM's role in moderation and governance.

Content moderation is an important case of application of Generative AI and LLM. It is a recent instance of using technological advances to improve our social lives. The critical analysis offered by this article affirms the necessity of combining technical explorations with normative inquiries from the socio-legal perspective. If the objective of LLM moderation is to assist online platforms to better govern their communicative space, then the research effort should not be fixated upon accuracy only. The literature from law and social sciences, such as the studies on platform governance, the division between easy and hard cases, and the conceptualization of legitimacy, supplies valuable insights to the research field of LLM moderation. Such an inter-disciplinary approach is indispensable for future studies of technologies and their impact on human society. 





