Legally Enforceable Hate Speech Detection for Public Forums
Authors: Chu Fei Luo
Rohan V Bhambhoria
Samuel Dahan
Xiaodan Zhu
Venue: EMNLP 2023 Findings (2023)
Keywords: hate speech
legal AI
human in the loop
large language models
prompt tuning
TL;DR: We propose a new formulation of hate speech detection aligned to enforceable definitions from legal entities, grounding our task in real-world consequences.

Abstract: Hate speech causes widespread and deep-seated societal issues. Proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforceable definitions of hate speech, which can make their outputs inconsistent with the goals of regulators. This research introduces a new perspective and task for enforceable hate speech detection centred around legal definitions, and a dataset annotated on violations of eleven possible definitions by legal experts. Given the challenge of identifying clear, legally enforceable instances of hate speech, we augment the dataset with expert-generated samples and an automatically mined challenge set. We experiment with grounding the model decision in these definitions using zero-shot and few-shot prompting. We then report results on several large language models (LLMs). With this task definition, automatic hate speech detection can be more closely aligned to enforceable laws, and hence assist in more rigorous enforcement of legal protections against harmful speech in public forums.

Legally Enforceable Hate Speech Detection for Public Forums
Chu Fei Luo 1,2 Rohan Bhambhoria 1,2 Samuel Dahan and Xiaodan Zhu
'Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen's 's University
CConflict Analytics Lab, Queen's University
CCrmell Law School
[chufei.luo, whwnbhoria, xiaodan. zhu, samuel. dahan dahan Jequennsu.ucummm
Abstract
Rag heads
Hote Speech (Input)
poison everything
Hate speech causes widespread and deep- seated societal issues. Proper enforcement of hate speech laws key for protecting groups of people against harmful and discriminatory lan- guage. However, determining what constitutes hate speech is complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforce- able definitions of hate speech, which can make their outputs inconsistent with the goals of reg- ulators. This research introduces a new per- spective and task for enforceable hate speech detection centred around legal definitions, and dataset annotated violations of eleven pos- sible definitions by legal experts. Given the challenge of identifying clear, legally enforce- able instances of hate speech, augment the dataset with expert-generatee samples and an automatically mined challenge set. We exper- iment with grounding the model decision in these definitions using zero-shot and few-shot prompting. We then report results several large language models (LLMs). With this task definition, automatic hate speech detection can be more closely aligned enforceable laws, and hence assist in more rigorous enforcement of legal protections against harmful speech in public forums."
Targeting ethnicity
X
Inciting violence
Legel
Identified Offence
Advocating genocide
Professional
Public Incitement of Hatred
CC 319 HR_AB x HR HR_BC BC
Externol
Legal Resources a
Enforceàble Low
(Target)
A visualization of our proposed method to ground hate speech to specialized legal definitions. A legal professional reads external legal resources and makes a judgement on some hate speech input, then identifies offences according to our definitions and makes judgement violations.
Figure 1:
speech laws, argue that automatic hate speech detection is essential for monitoring these forums due to their large scale. This work analyzes the effectiveness of machine learning methods on en- forceable hate speech laws, where enforceable is defined as a law or rule that is "possible to make people obey, or possible to make happen or be accepted," "3 We believe the advancement of ar- tificial intelligence speech by better aligning with regulations that pro- tect groups of people against harmful discrimina- tion, and hence help build healthy society.
1 Introduction
Hate speech is regarded as "a denial of the val- ues of tolerance, inclusion, diversity and the very essence of human rights norms and principles." #2 The internet and public forums are global assets that facilitate interaction between people, but the ease of communication also enables hate speech to travel rapidly and spread large scale, causing serious societal and security issues (Rapp, 2021; Alkiviadou, 2019). To ensure compliance with hate
be utilized to tackle hate
Definitions of hate speech can vary to the point that two datascts are inconsistent with each other (Khurana al., 2022; Fortuna et al., 2020). Pos- sibly due to these divergences in hate speech def- initions, previous works find that models trained on one dataset often perform poorly when evalu-
'The code for this paper publicly available at https: //gi thub. http://hhhellloeeglaaaaaaee /htpps//www.n..cc www.a/or////ggeo/eee.oeo. peeventin-vhhy-ccclll---tt---e spaccc/mmamccatt-addd-
http:///ddiciioaarycaarriiggeeee /nttsss///iitionary..miiiiii english/enforceebbl
'nttps://dictionary

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ated on another (Yin and Zubiaga, 2021; Kim et al.. 2022). Sachdeva al. (2022) note this is due to intersubjectivity in annotated datasets - there high variability in annotator opinions on highly complex tasks, including hate speech detection. This subjectivity is not specific to computer sci- ence; hate speech is a highly debated topic, with definitions varying significantly across regions and organizations (Brown, 2015; Zufall et al., 2022). Previous works try to remove these variations with modelling strategies like contrastive learning (Kim et al., 2022) or data perspectivism for de-biasing (Sachdeva et al., 2022). However, they define hate speech concepts that have not been explic- itly aligned to legal definitions. When models are not trained with awareness of the law, the output is likely irrelevant and not in line with the accepted and enforceable legal definitions under concern.
dataset with automatically generated labels, and challenge dataset for unsupervised methods such as self training.
Empirical baselines on the state-of-the-art large language models (LLMs) have been established to facilitate further studies on the problem. We report results with task instruction prompting, zero-shot prompting, and parameter-effiiient finetuning (PEFT).
Related Work
Hate speech Hate speech has significant societal impact. Automatic hate speech detection is essen- tial in regulating large online communities such as social media (Yin and Zubiaga, 2021). How- ever, there are significant issues with dataset qual- ity (Khurana al., 2022; Fortuna et al., 2020; Yin and Zubiaga, 2021) due to the inherent subjectiv- ity of hate speech (Sachdeva al., 2022; Brown, 2015). Hate speech is also highly complex, and various works choose to focus on one component. ElSherief et al. (2021) target implicit hate speech, and Yu et al. (2022) demonstrate that context can often change the degree of hate in a statement. There are several strategies mitigate intersubjec- tivity, forming three approximate groups: task defi- nition, model training, and data pre-processing. For task definition, the research introduced Khurana et al. (2022); Zufall et al. (2022) is the only work to our knowledge that defines a detailed annota- tion framework in collaboration with legal experts and social scientists. Zufall et al. (2022) propose framework around the EU's definition of hate speech, which is the minimum standard in Europe. Modelling strategies like contrastive learning (Kim et al., 2022) reduce variance the embeddings, and few-shot methods reduce requirements for training data (AIKhamissi et al., 2022). Finally, Sachdeva et al. (2022) propose data-level de-biasing method to reduce biases between annotators based on their demographics. In contrast, our work attempts to reconcile multiple legal definitions of hate speech within one jurisdiction.
In this work, we introduce a new task formula- tion grounded in eleven definitions of hate speech curated from criminal law, human rights codes, and hateful conduct policies from public forums. We provide gold label dataset for violations of these hate speech definitions, annotated by legal experts. Statements that could lead to punitive consequences are generally rare, so we augment the positive class with edited samples from our expert annotators, and employ data filtering meth- ods to increase the size. Due to the expense of domain expert annotation, instruction-tuned LLMs are a reasonable candidate for hate speech detec- tion. They have shown promising results on legal tasks with prompting (Guha al., 2022), and in- creasingly long context windows allow models to process longer pieces of text (OpenAI, 2023). We report baseline performance using our definitions to prompt the state-of-the the-art LLMs, and we also use parfmeter-efficient tuning (Taori et al., 2023; Hu et al., 2021) and self training to improve the performance of smaller LLMs. Additionally, we construct silver dataset with automatically gener- ated labels. The contributions of our research are summarized follows:
We propose a new perspective and task for hate speech detection grounded in legal definitions to adhere with accepted enforceable standards.
Prompting Prompting has shown great success in zero-shot and few-shot settings (Brown et al., 2020). Although there are concerns to the efficacy of prompts (Webson and Pavlick, 2022; Khashabi et al., 2022), incorporating natural language in- structions in the input text is a common strategy to improve zero-shot performance (Chen et al., 2021).
We built a novel dataset for this task, with an- notations for eleven legal definitions across sev- eral jurisdictions and platforms. We provide a gold dataset annotated by legal experts, a silver

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Guha al. (2022) show LLMs can obtain reason- able performance based on task definitions for spe- cialized tasks in law.
10 ToL
may oogroncy
With the emergence of instruction-tune language models trained on human feedback, large language models have seen emergent capabilities in zero- shot settings with instruction prompting (Ouyang et al., 2022; OpenAl, 2023). However, there is distinction between a model's ability to understand language and reason (Mahowald et al., 2023). Early studies show that large language models are still insufficient on many high-stakes tasks (Li et al., 2023). Additionally, the imprecision of natural lan- guage outputs often hinders non-generative tasks like classification. Hallucinations occur when model is uncertain of its decision but does not know how to express itself, or does not have proper rea- soning for the task (Mukherjee et al., 2023). To this end, many works attempt closer supervision the model's ss thought process, including chain-of- thought prompting (Wei et al., 2022) and process supervision (Lightman et al., 2023).
iceneny
Figure 2: Taking our five social media policies as exam- ples, illustrate the overlaps and differences between various protected groups. Most definitions have at least one unique protected group.
Some jurisdictions have human rights codes separate from criminal laws. While these infractions do not typically lead to incarceration, violations often result in mone- tary compensation for victims. We collected codes from four Canadian provinces and territo- ries. Again, the research approach can be easily extended to other code.
Human Rights Code:
Eaffiietr-E.fficientt Tuning Many previous studies observe that fine-tuned model performs better than zero-shot or few-shot prompting (Li et al., 2023). LLMs like ChatGPT (Ouyang et al.. 2022) or LLAMA (Touvron et al., 2023) require sig- nificant computational resources to update parame- ters. This makes srrammter-efffciere tuning meth- ods highly desirable to increase the efficiency of model training. LLMs such LLAMA with 7 bil- lion parameters (Taori et al., 2023), can be tuned on consumer hardware with Low-Rank Adaptation (LoRA) (Hu et al., 2021).
Hateful Conduct Policies: Terms and Condi- tions for content posted to social media. Viola- tions can result in removal of the problematic content, and suspension/remooaal of the account in serious cases. We collected policies hateful conduct from five social media platforms, as of December 2022.
Each definition is comprised of two components: a description of unacceptable behaviour or con- duct, and a list of protected groups under their policy. Human Rights Codes describe violation as any statement "likely to expose individual or class of individuals to hatred contempt," whereas Hateful Conduct Policies protect against content that "targets a protected group." While these are phrased distinctly, the offending behaviours de- tailed in these policies are very similar to each other, so the primary distinction between most of our definitions is in their protected groups. As illustrated in Figure 2, there are more common protected groups, like Race, Sex/Gender, Caste (family status), and rarer groups like Veteran Status or Serious Disease. The Criminal Code describes more severe offences of promoting genocide or public incitements of hatred that are "likely to lead to a breach in peace."
3 Enforceable Hate Speech Detection
Our research aims to detect hate speech statements that violate at least one legally enforceable defi- nition of hate specch, which we call Enforceable Hate Speech. This is illustrated in Figure We utilize eleven definitions hate speech from three legal sources, which are applicable to language and conduct published on public forums. For legal details, please refer to Appendix A.1.
Criminal Code: Federal laws that estab- lished by a country's legislature and codifies the country's 's criminal offences. Violations can re- sult in criminal charges. In this work, we use the Canadian Criminal Code, but the framework can be easily extended.

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Pace
please refer Appendix A.3. There were four sam- ples of identical text from different sources, likely cross-posted between Reddit and Twitter, but they only appear once in our dataset, and we confirm their original datasets give these four overlapping samples the same annotation labels. For this reason, we count one positive sample twice in Table 1.
belifrasseciatonn
National ethnk origin
identity
Other
Sesuel orientation
Religion/eel glous belees
Figure Diagram showing frequency protected groups that appear in our dataset, annotated by legal experts.
Enforceable Annotations. We collaborated with volunteer legal experts over two rounds of an- notations. They are law school graduate students with an average of two years of education into their J.D. degree. We distributed posts to each expert in batches, such that each post would have at least three unique experts, and obtain label with major- ity voting. While there are many clear statements of hateful speech, there are also times when the speaker's original meaning cannot be discerned, but the phrase contains hateful language or ambiguous implications that an expert believes would require further context. Hence, we provide an Unclear option for three possible classes: Violates, Does not Violate, and Unclear. Additionally, we ask the annotator to identify protected groups being targeted, and provide their reasoning for violation in free-form text. We employ or-wise combina- tion of all annotators, where a group is selected if at least one annotator believes they were targeted. Then, the annotators mark the specific definitions violated. For detailed annotator instructions, refer to Appendix A.2.
3.1
Data collection
Our dataset contains samples from existing datasets with the aim to determine how previous annotations align with legal definitions of hate speech. Addi- tionally, doing gains greater semantic variety as we sample from a diverse set of public forums. Next, we obtain annotations from legal experts. We employ editing annotation tasks and data fil- tering to increase the size of our gold data, while utilizing experts to obtain high-quality labels with detailed reasoning. Finally, we further sample silver dataset with automatically generated labels.
Non-enforceeale Datasets. We use five English hate speech and toxicity datasets released in the public domain.
CivilComments (Borkan al.. 2019) was sourced from the Civil Comments database, comment widget that allowed peer-reviewed comments on news sites. We randomly mined comments from the CivilComments train dataset that had some toxicity value above 0.5. SBIC (Sap et al., 2020) was obtained from Gab and Twitter. They do not have direct labels for hate speech, but we infer heuristic for posi- tive cases based on previous works (AlKhamissi et al., 2022) - that is, offensive language that targets a group is considered a positive sample. HateXplain (Mathew et al., 2021) was created from Twitter for the goal of evaluating inter- pretability methods.
After the first round of annotations, there were 488 total hate speech statements, with 22 (4.5%) samples in the positive class, and 28 (5.7%) la- belled Unclear. We note that our positive rate is lower than prior datascts over all source datasets except for HateXplain. Some previous datasets such as IHS and CounterContex! focus micro- aggressions and implicit statements, which are the main focus of Implicit Hate Speech and Counter- Context. We argue these works not aligned to the legal concept of hate speech, instead capturing the broader concept of discrimination (prejudice towards certain groups of people). We internally discussed the enforceability of discrimination ver- sus hate speech at great lengths before removing discrimination policies from our definition. Dis- crimination is a broad concept and its impact on society is varied, making the legal implications not well defined.
Implicit Hate Speech (IHS) (EISherief et al., 2021) was collected from Twitter, including fine- grained labels for various types of hate speech.
CounterContext (Yu et al., 2022) was acquired from Reddit, by collecting pairs of samples to provide more context within the public forum.
The distribution of our dataset is shown in Ta- ble I. For further details on public forum statistics,
The following is a lubel-flipped sample from Implicit Hate Speech:

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"this crazy. how do you give a loan someone who can be deported at will ?"
Dataset
Count
Pos-o
Pos-l
Unel.
CivComments
418
(0.3)
17 (4.5)
SBIC
20
9 (45
(1000)
(5.0)
The speaker believes "someone who can be de- ported at will" (immigrants) should not be able to get loans, which is discrimination but not hate speech. Hate speech in the law is defined as di- some-
HateXplain
20
(40.0)
(50.0)
(13.0)
IHS
20
9 (45.0)
0 (0.0)
(0.0)
CounterContext
10
7 (20.0)
0 10.0)
(0.0)
Total with Augment.
488
39 (7.9)
22 (4.5)
28 (5.7)
704
165 (23.4)
38 (5.4)
reetly haaaliig/eehmmaaiinnnnnmiianinii one based on protected attribute. Multiple defi- nitions reference, for example, comparing a group of people to insects. Since previous works do not reference the law, they can conflate different legal concepts, and our understanding of hate speech in the NLP community becomes misaligned with experts in other disciplines.
Dataset distribution by sampling source, as well as comparison of positive rates by previous hate speech definitions (Pos-o) enforceable legal definitions (Pos- D). Uncl. refers to Unclear label rate. Each entry is reported the format Value (Rate %)
Table I:
Automatic Augmentation. Recent works have shown that the state-of-the-art LLMs are able to generate high-quality labels (Mukherjee et al., 2023), so we extend our dataset with raw Red- dit data and silver labels from zero-shot prompt- ing. We scraped Reddit submissions and comments from February 21, 2022 to September 5, 2022. We chose 10 random but popular communities (also known as Subreddits), as well as 10 that were in- dexed as high toxicity by Rajadesingan et al. (2020). Many of the Subreddits from their work have been removed, but we were able to find 10 active forums with some positive toxicity score. We collected both submission text and comments for 7.8 million posts total. From this set, sampled 100,000 posts with maximum cosine similarity to least one of our gold label positive samples Tmar 0.55. We then generate labels with the current state-of- the-art language model, GPT-4, as we found it pro- duced high quality labels. We use the prompt tem- plate from our experiments, as shown in Table 2, and multi-target prompting described in Section 4. During this process, we also sample obvious neg- ative samples in similar way. If the maximum cosine similarity of one sample to all of our gold positives is less than a threshold Tiii = 0.3. this sample is saved as an easy negative. We use these easy negatives to increase the size of our gold data, such that our dataset matches the organically col- lected positive rate. This automatic augmentation results in a gold dataset with 2,204 samples, and a silver dataset with 2,007 samples.
Expert Augmentation. We wish to further scale the size of our data while minimizing requirements for expert annotation. To strengthen the training signal, we asked the annotators to edit existing posts in the dataset that were not hate speech such that it would become violating post. First, we se- lected posts that either had minority vote for the positive class, or least one Unclear annotation. We obtain 80 such samples from our first round of annotations, and instructed the annotators to edit the statements to specific definitions. Where they were unsure of how to edit the post, we asked the experts to create new statements.
This process is repeated with 1,100 samples of Red- dit data, which is subset of the automatically ob- tained data described below. We divided the posts among the annotators, asking them edit the posts to become hate speech. The annotators noted some difficulty with the editing task, so for the latter half, we asked them to only make edits that changed less than half of the statement. For example, replacing a neutral insult such as "jerk" with a slur could change the statement to hate speech. Overall, we obtained 179 edited statements from the experts. Additional positive samples of hate speech were obtained from HateXplain, which had the greatest correlation to our positives from our initial round of annotations. We also re-incorporate our expert edits, along with the expert's own annotation of vi- olating categories. To ensure quality in our edited statements, we obtain two additional annotations per edited sample and perform majority voting. Af- ter this process, we have 165 positive samples, as shown in Table 1. The most common groups tar- geted are illustrated Figure 3. These groups are protected by all hate speech definitions, except for "Political belief/association.
Methodology
4.1 Classification with Legal Reasoning
Classification.
We consider a bascline of zero- shot classification with prompted instructions.

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Definition
[target] states [description]. Pro- tected groups include [groups].
heads, trained 1.5 trillion tokens from novel dataset, RefinedWeb. We run their 7b model in our experiments.
Classification
Is this post a violation of [tar- get]? Please answer Yes, No, or Unclear.
WizardLM (Choi et al., 2022). This language model was trained on data, refactored with more complex instructions generated by LLMs. Since our task is highly contingent on understanding instructions, choose their model as strong candidate baseline. We utilize their 13-billion parameter model.
Reasoning
Is this post a violation of [target]? Please answer Yes pesneer If Yes, explain why.
Table 2: Templates used for all experiments. [target] is arbitrary name chosen strengthen the relationship between the definition and question. [description] and [groups] are components of the definition. One prompt can have multiple definitions.
Llama 2 (Touvron et al., 2023). Currently the state of the art in open source language models, this is a model trained with a novel Grouped- Query Attention mechanism and longer context length compared to its predecessor. We use the 1lama-2-chat-13 3b checkpoint.
Given set of enforceable definitions D, at- tempt to predict whether a statement violates one of the policies. We perform coarse-grained and fine-grained classification.
GPT-3.5 (Ouyang al., 2022). We use the gpt-3.5-turbo- model, with 8K and 16K context windows available. These two models were trained with human feedback from GPT-3 with 175B parameters.
For all experiments, we prompt the model with input text and the target definitions, using uni-
fied template shown in Table 2. To be precise, any
GPT-4 (OpenAI, 2023). GPT-4 is an autoregres- sive LLM trained with further human feedback. The exact number of parameters is unknown, but it is known to be larger than GPT-3.5.
generated answer starting with Yes is a positive prediction, and any with No is an explicit nega- tive. Hallucinations, where conditional generation models generate outputs outside of the set of target responses (Ji et al., 2022), is an important measure for model robustness, so we report hallucination rate in our responses. We explicitly ask the model to respond with Yes, No, or Unclear, as shown in Table 2, and define hallucination any response that does not begin with any of these options. Specifically, we consider the following typical large language models:
Legal Reasoning. Increasing points of thought and reasoning steps have been noted to significantly improve performance in LLMs, shown in both model training with emergent papers like process supervision (Lightman et al., 2023) and zero-shot inference like chain-of-thought prompting (Wei et al., 2022). In this work, we prompt the model to explain its classification. We choose to provide minimal guidance in the prompt as a baseline, and to observe how the language model responds to uncertainty. For this setting, we add an additional hallucination criteria where the model does not out- put any explanation.
RoBERTa-Large (Liu et al., 2019). As a base- line, we train pre-trained RoBERTa model with 355M parameters. RoBERTa-Large is encoder-only LLM, while the others have au- toregressive encoder-decoder architectures. To obtain comparable zero-shot results, we use the LM-BFF method (Gao et al., 2021).
We want examine the quality of the reasoning by mapping to the definition components, i.e. the protected group being targeted. We obtain bag-of- words representation of the response as a set of the word tokens W, then automatically map relevant words to protected groups G. For example, if a model's explanation mentions women, this implies the protected group of gender. We construct set of search words S and map each one least one protected group g E G. First, we gather set of all protected groups from our definitions and manually map them to larger categories, as shown
Alpaca-LoRA (Taori et al., 2023). We use an open-source version of Alpaca, which is LLAMA trained with instruction tuning (Touvron et al., 2023; Ouyang et al., 2022). This version is repro- duced with Low-Rank Adaptation (LoRA) (Hu et al., 2021). We use the 7-billion parameter variant of LLAMA as the base model.
Falcon (Penedo et al., 2023). Falcon an open-source LLM with multi-query attention

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in Table 9. Next, we gather all non-stopwords from the LLM responses. We search the words with ConceptNet (spe, 2017), and if there is a one-hop relationship between this word and one of those in Table 9, we add that to the searchable mapping. In this way, we obtain searchable keywords like young/old, men/women, christianityjjudaisnnnnn etc.
to gain a sense of the dataset's difficulty without mentioning the definitions.
Self-training (ST). With the silver data, we employ a self-training scheme to further im- prove performance. We use simple self train- ing routine, inspired by (Wang et al., 2021; Zou et al., 2019). With the best performing baseline checkpoint as the teacher, we generate inference pseudo-labels for our unlabelled data. Then, we use easy example mining (Kumar et al., 2010) to find 1,000 samples with the highest model pre- diction confidence. We train the checkpoint for 2 epochs as the student, then use the new model the teacher and regenerate pseudo-labels. This process is repeated for rounds, until loss con- vergence. To compensate for noisy labels, we use label smoothing in the cross entropy loss.
Unv
Ja, V)
(I)
MUV
Single-target vs. Multi-target. A long-standing challenge in legal tasks is processing long, com- plex documents such as court opinions that often contain irrelevant information (Luo et al., 2023). To alleviate this issue, we refactor our classification to present one definition of hate speech at a time with a single-target configuration. For an input- output pair (x, y) E X with fine-grained labels y {y1: 1/2 --. yn annotated to set of defini- n, refactor the output to create pairs {(x y1 ), (I,, y2..... (Ir y. We prompt the model with each pair individually, then gather them back into one vector. For coarse- coarse-grained labels, we combine the decisions over 11 definitions by ma- jority voting. Additionally, recent language models have been proposed with longer context windows (OpenAI, 2023), and we wish to evaluate their abil- ity reason over multiple targets. Where available, we compile all definitions into one prompt and ask the model to provide reasoning simultaneously.
Results and Discussion
5.1 Classification Results
tions D
Coarse-grained Performance Our experiment results summarized in Table 3. The best per- forming model is GPT-4. Due cost considera- tions, we were only able run one round of exper- iments with GPT-4, so these results are considering the multi-target performance rather than single tar- get. The two variants of GPT-3. 5 perform similarly. but the 16k context variant performs slightly worse in fine-grained classification. Also, the 16k model exhibited 0.5% more hallucinations. Of the smaller models, Falcon-7b performs the best, while the macro-fl scores for Alpaca-LoRA and WizardLM are comparable. Notice that Alpaca-LoRA has very minimal hallucinations, which suggests larger mod- els are not strictly necessary to achieve low hallu- cination rates. However, lower hallucination rates are not indicative of higher performance, and there is considerable performance gap between larger models, with over 100 billion parameters, and the smaller variants with less than 15 billion.
4.2 Fine-tuning
We investigate fine-tuned language models to help establish baselines and provide insights for our task. In this work, we tune the encoder-only RoBERTa-Large (Liu et al., 2019) with parameter- efficient tuning over three settings:
LM-BFF (Gao et al., 2021). We adopt the LM- BFF method as it allows us to prompt encoder- only language models in similar format as autoregressive LLMs. This method takes the prompt text as input but appends <mask> token to the end, converting the prompt response to a masked language modelling (MLM) objective. This method allows BERT to achieve similar per- formance to the original GPT-3.
After self-training with silver data, coarse- grained performance of RoBERTa-Large improves significantly. This demonstrates the coarse -raninee labels generated by 44 are generally high qual- ity. We observe RoBERTa-Larga does not predict the Unclear class after self-training. The Unclear class is rare, especially after our positive class augmentation, RoBERTa-Large can outperform GPT-3.5 by not predicting this class.
Tuning (T). We also tune RoBERTa-Large on the silver data with noisy labels generated by GPT-4. We freeze the RoBERTa-Large weights and only tune the encoder. This setting is chosen
Fine-grained Performance. For fine-grained performance, there is a similar trend between larger

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Coarse-graine grained
Fine-gramme Fine-grained
Fine-grained reasoning
Model
Ma-flf
Ma-P
Ma-R
Mi-fI 1 HR J Ma-fl
Ma-P
Ma-R
Mi-fl
Ma-ff1
Ma-P
Ma-R
Mi-fl
HR
GPT-3.5
30.7
46.5
52.5
27.1
0.05
45.4
30.0
96.5
45.3
31.8
19.1
99.7
32.3
0.6
GPT-3.5-16k
30,9
31.1
43.3
73.9
0.6
43.4
28.2
97.8
44.2
33.7
20.7
97.1
32.6
0.6
GPT-4
41.4
38.1
48.0
92.5
0.04
52.2
43,9
67.7
$5.1
0.04
Falcon-7b
17.4
25.1
24.6
39.7
14.0
12.1
7.0
46.1
12.1
10.0
6.0
31.6
10.2
63.4
Alpaca-LoRA-7b
21.1
28.6
34.7
46.4
0.06
15.1
8.4
87.1
14,8
10.5
5.6
92.9
10.9
21.7
MizardLM-13b
9.2
28.5
25.9
11.3
28.5
20.2
11.8
78.1
15.2
15.2
8.4
87.1
14.8
75.6
Llama2-13b
8.5
26.8
24.7
16.3
24.2
13.5
7.5
74.0
13.8
20.8
12.8
59.5
21.1
$4.2
RoBERTa-L (LM--BF))
5,4
35.8
33.7
8.5
8.8
4.8
62.7
9.1
RoBERTa-L (T)
33.3
0.6
0
0
RoBERTa-L (ST)
32.0
32.2
33.3
Random
22.6
30.8
33.3
46.3
10.6
6.0
51.0
10.7
Table 3: Summary of performance over our classification tasks. Results are single-target, except GPT-4 which reports one multi-target run. 1 indicates higher score is better, and + indicates a lower score is better. Ma-fl, Ma-P, and Ma-R are Macro-fl, Macco-Precision and Macro-Recall respectively, HR hallucination rate, in %. RoBERTa-L refers to RoBERTa-Large.
Course grained
Reasoning
works to further investigate this behaviour.
Model
Ma-fl f Mi-fl f
HR 1 1
5.2 Reasoning Capabilities
GPT-3.5
31.5
77.0
0.10
37.2
GPT-3.5-16k
31.3
76.6
0.04
36.4
Explanations. Next, we report the same exper- iments but prompt the model to explain its deci- sion. When we ask the model to demonstrate its reasoning, the performance actually decreases on average. With additional investigation, we find that the models tend to hallucinate more when asked provide an explanation WizardLM, es- pecially, begins to repeat the prompt back before generating the expected response. This might be because the WizardLM model is trained on highly complex tasks, while our instruction ("If yes, ex- plain why.") was vague compared to the three- way Yes/No/Unclear decision. We sample out- puts from different language models, presented in Table and find that the quality of reasoning is not the same as our legal experts. Even GPT-4 makes frequent mistakes lation of CC_318 (advocating genocide), but the statement and its own explanation do not reflect the severity of that charge. Legal texts often have complex and rare words, so zero-shot LLM might benefit from adding definitions of words or other detailed instructions in-context.
GPT-4
41.4
92.5
0.05
38.4
Table 4: Summary of our multi-target experiments, where we provide the entirety of our external legal refer- ence to the model via prompt. Ma-fl refers to Macro-fl, Mi-fl is Micro-fl score, HR is hallucination rate, and is the Jaccard score for recognizing protected groups.
and smaller models, and GPT-4 still has the high- est scores. It is interesting to note that GPT-3.5 5 and 395-3. perform similarly, which indi- cates longer context length does not necessarily lead to stronger understanding of the input. This agrees with previous work in legal applications that showed specialized encoder-only models like Longformer do not outperform BERT (Bhambhoria et al., 2021; Luo et al., 2023). Also, WizardLM out- performs the other open-souree models in Macro-fl score, and achieves double the performance of ran- dom guessing. While the GPT-series models seem out-perform other works by wide margin, the smaller models perform well relative to their size (7-13B parameters compared to 175B).
the model identifies a vio-
After fine-tuning RoBERTa-Large, the fine- grained performance reaches O due to model col- lapse. This is likely because our positive samples become too rare per definition for effective fine- tuning. We attempted various training strategies like upsampling the positive class, but the perfor- mance does not improve. In general, despite reach- ing similar coarse-grained performance, seems like our task is difficult for smaller models to under- stand with naive training methods. We urge future
Multi taltietarget Results. Comparisons on GPT se- ries models for long, multi-target prompts are sum- marized in Table 4. Due to cost considerations, only collected multi-target results GPT-4, the results are identical compared to Table 3. The results of the two GPT-3. models improve, with GPT-3.5 dramatically improving in Micro-fl. The two GPT-3.5 5 models produce significantly fewer Unclear predictions in the multi tartit-tttettt setting. Per-

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Sumple
Did he kill your life? Women are objects so you can just buy another one anyways.
Limitations
Though the model is trained on legal definitions of hate speech, the application of these definitions to the facts remains subject to interpretation, and thus our outcome may not necessarily reflect the similar work trained a different dataset. As observed when training on of distribution data, our def- inition of hate speech is still incompatible with other works such as (Sachdeva et al., 2022). Our definition of hate speech could still be considered incomplete, as we do not consider definitions from the EU. As noted in section 3, nine of eleven defi- nitions (including the five from social media com- panies) define very similar behaviours, to the point where we homogenize them in second round of annotations. This might indicate our sampled definitions are culturally monotonous. In fact, hate speech an inherently subjective concept, and we are aware of the biases our annotators hold in their decisions. For example, human rights codes and criminal law focus on defining protected groups, but violations are decided by a jury laypeople when case is brought court. We follow dataset annotation conventions, i.e. majority voting with opinions from least three legal experts, to simu- late a court decision.
Source
Response
GPT-3.5
Yes, this post violation of TOS RED. It promotes hate based on gender and objectifics women, which goes against the rule on hate and promoting attacks of hate.
GPT-4
Yes. this post violates several of the above policics. 1. CC_318 318 and CC_319: This post can seen us pro- moting hatred and disrespect towards women, which is a protected group under these policies. devalues women by referring to them us objects that can be bought, which can be seen as advocating for their mistreatment...
WizardLM
Yes, this post violation of CC_318 it promites the idea that women are objects to be used and dis- carded, which is an act that falls under the definition of genocide.
Annotator 'Wilful promotion of hatred', 'degrades women', "The view of women objects rises the level of ardent and extreme feelings constituting hatred required to uphold the constitutionality of prohibi- tion of expression human rights legislation'. 'Hate speech and discriminatory against identifiable group'
Table 5: A comparison of chosen language model out- puts and our legal experts' notes for a gold data sample.
haps with a single definition, they are more likely to produce "neutral" responses to eir on the side of caution. However, when at the limits of their reasoning capability, the models tend to be more decisive. Previously observed issues with hallueina- tions are also shown with these results. The Jaccard similarity less than 0.5, indicating more than half the model's 's predictions and the gold annotations do not align on average. Here, the performance of GPT-4 is similar to both versions of GPT-3.5.
Additionally, the baselines evaluated in this work have many limitations to be addressed before real- world implementation. Since we have limited train- ing data, and mainly report zero-shot results, the performance and perspectives of the model are sub- ject the pre tre-training data. There are also issues inherent the model, including hallucinations. lack of interpretability, among others. The data not very suitable for tuning LLMs as well the prompts are very monotonous, so the model can very easily start producing hallucinations.
6 Conclusion
In this work, we introduce new task formulation for hate speech detection grounded in enforceable legal definitions and release a gold label dataset annotated by legal experts. The strictness of these definitions results in a lower positive rate compared to previous annotated datasets. We augment the data with expert edits and automatic data augmen- tation. Then, establish baseline performance on the state-of-the-art LLMs and tune encoder-based models. Finally, we tune with our silver data to fur- ther improve performance. We observe that LLMs perform impressively, but closer inspection of their explanations shows they lack reasoning. This work addresses the importance of legally enforceable hate speech, and we urge future work in this area.
Ethics Statement
Intended Use. We see least two applications for legal practice. First, this system could be used by social media companies to address or at least alleviate the issue of hate speech their platforms for hate speech. Since the annotations are aligned with legal definitions, such legal AT provided it is aligned with the accepted legal interpretation of hate speech might help the most egregious misconduct well as offer some legal legal jus- tification. Perhaps, its output might even be used as court evidence in court. Another application is an open access tool, such as add-on available on

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website browser. Such tool might be used by pub- lie forum users who might have been victims of hate speech violations. They would input the text and the system would identify the relevant lsw(s), determining whether the statement constitutes hate speech.
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2019. Nuanced metrics for measuring unintended bias with real data for text classification. CoRR, abs/1903.00461
A. Brown. 2015, Hate Speech Law: A Philosophical Examination. Routledge Studies in Contemporary Philosophy. Taylor & Francis.
Failure Mode. Due to the high stakes nature of the task, a false negative could lead disastrous real-world violence against groups targeted by hate speech. Likewise, a false positive could lead to more severe consequences than someone deserves, which weakens user confidence and morale. How- ever, the system is designed to be used to augment and assist human investigations, and these scenar- ios are unlikely to occur with sufficient human in- tervention.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33: 1877-191...
Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z Pan, and Huajun Chen. 2021. Knowledge- aware zero-shot learning: Survey and perspective. arXiv preprint urXiv:2103.000
Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Mal- masi. Oleg Rokhlenko, and Eugene Agichtein. 2022, Wizard of tasks: A novel conversational dataset for solving real-world tasks in conversational settings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1514-3529. Gyeongju, Republic of Korea. International Com- mittee Computational Linguistics.
Misuse Potential. As mentioned above, there is a chance for others to use these methods to com- pletely automate regulation of their platforms and enact immediate consequences. Overconfidence in model predictions and insufficient understand- ing of the limitations of our work could lead to severe consequences in cases of failure. With the rising popularity of LLMs, is important further ground the model and prevent misuse.
EU Commission al. 2016. Code of conduct on coun- tering illegal hate speech online. European Commis- sion, Bruxelles, Belgium.
Acknowledgements
Mai ElSherief,
Caleb Ziems, David Muchlinski, Vaish- navi Anupindi, Jordyn Seybolt, Munmun De Choud- hury, and Diyi Yang. 2021. Latent hatred: A bench- mark for understanding implicit hate speech. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 345-363, Online and Punta Cana, Dominican Republic. Asso- ciation for Computational Linguistics.
The research is in part supported by the NSERC Discovery Grants and the Research Opportunity Seed Fund (ROSF) of Ingenuity Labs Research Institute at Queen's University.
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Rohan Taori, Ishaan Guirajani. Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca:
A Additional Data Details
A.1 Legal definitions of Hate Speech
llaaa model. https:// An instruction-followt http///tafffddddaaaae/aa gi ght.. com/ hatpe//ttttulab//ttt
Criminal Code. We use two sections of Cana- dian Criminal Code, Advocating Genocide and Public Incitement of Hatred."
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Human Rights Codes. In Canada, human rights codes are maintained by provincial governments, and we select four that specifically mention hate speech: British Columbia, Alberta, Saskatchewan, and Northwest Territories. Other provinces' codes that mention discrimination without discussing ha- tred or contempt not considered.
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Hateful Conduct Policies. The European Com- mission established Code of Conduct in partner- ship with four social media companies to facilitate compliance with EU code, including adherence the definition of hate specch defined by the EU (Commission et al., 2016). In addition, there is the Framework Decision on combating racism and xenophobia. The Framework Decision sets out min- imum standards for the criminalization of racist and xenophobic speech and behavior, and requires EU Member States to adopt legislation criminalizing certain types of hate speech and hate crimes (of the European Union, 2008). Finally, the President von der Leyen announced in september 2020 the Com- mission's intention to propose to extend the list of EU crimes or Eurocrimes to all forms of hate crime and hate speech. We note that most social media companies also have more comprehensive standards for their content as drafted by their inter- nal legal teams. We collect definitions from Twitter. Reddit, Youtube, Meta, and Tok.
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A.2 Annotations
The annotators are ten law students (carly-mid 20's) who volunteered for the project knowing their work might be used in academic publications. The stu- dents were either rewarded with credits as part of practicum course, or paid the minimum wage in our region depending on the batch of annotations they participated in. For transparency, we did have one annotator that volunteered out of interest for the project, and they contributed <2% (<50/ 2100) of the annotations, 5 hours of work total (including meetings, training, and actual annotation work).
Yang Zou, Zhiding Yu. Xiaofeng Liu, BVK Kumar, and Jinsong Wang. 2019. Confidence regularized self-training. In Proceedings of the IEEE/CVF In- lernational Conference on Computer Vision, pages 5982-5991.
Frederike Zufall, Marius Hamacher, Katharina Kloppen- borg, and Torsten Zesch. 2022, A legal approach to hate speech- operationalizing the EU's legal frame- work against the expression of hatred as an NLP task. In Proceedings of the Natural Legal Language Pro- cessing Workshop 2022, pages 53-64, Abu Dhabi, United Arab Emirates (Hybrid). Association for Com- putational Linguistics.
During annotation, they were provided word document containing definitions and relevant ex-
*Criminal Code, R.S.C. 1985, C-46, s. 318-319

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Forum
Count
Pos-o
Pos-I
Unel.
B Additional Experimental Details
CivilComments
418
10 (2.4)
(4.1)
B.1 Hyperparameters
Twitter
39
14 (35.9)
3 (7.7)
5 (128)
Reddit
16
11 (68.8)
0
2 (12.5)
We use the Adam optimizer with a learning rate of 5e-6, weight decay of 0.01, and training batch size of 8. We train for 10 epochs, which can take 10-15 hours for 4000 samples. When there are mul- tiple target tasks available, we interleave datasets such that there is an even distribution during train- ing. The open-source LLM experiments were per- formed on Nvidia 3090 GPUs with 24GB of RAM.
Gab
13
(61.5)
(69.2)
(23.1)
Stormfront
0
(50.0)
Total
488
39 (7.9)
22 (4.5)
(5.7)
with Augment. 704
165 (23.4)
38 (5.4)
Table 6: Dataset distribution by source forum, as well comparison of positive rates by previous hate speech definitions (Pos-o) to enforceable legal definitions (Pos- D). Unel. refers Unclear label rate, and all numbers are reported as Value (Rate %6..
We use inference APIs from OpenAl for zero- shot LLM experiments, All OpenAl models are the versions released on June 13th, 2023, i.e. GPT-3.5 refers to model ID apt-3.5-turbo 0613, GPT-16K-3.5
amples or caselaw, and the project was supervised by a law professor. First, annotators were asked to identify if the statement violates legal definition of hate speech. If there is violation, they were asked to indicate which definitions have been vio- lated. Then, they were asked to explain their choice free text form, and/or highlight free span text segments of the post they deemed most important to their decision. For posts where majority voted positive, we pool the fine tne-grained definition labels into set per sample. If at least one expert deems definition has been violated, then it is assigned that fine-grained definition label.
5pt-3.5-turbo-16kk
and GPT-4 gpt-4-0613. We use decoding temperature of 0.1 and a top-p value of 0.75. For text preprocessing, we remove Unicode (except for " and ", which we replace with their plain text counterparts), anonymize any username references, and decode HTML strings (eg. &amp; becomes &). We use the tiktoken library check the token length of all inputs, and we truncate text with length greater than 400 tokens.
We provide Unclear label since we believe it is important for an automatic system be able to recognize when piece of text is ambiguous. While there are many clear statements of hateful speech, there are also times when the speaker's 's orig- inal meaning cannot be discerned, but the phrase contains hateful language or violent implications that causes concern, and a human expert believes it would require further context. The inter-annotaton agreement, taken Krippendorff's alpha, is 0.623 from the initial round of annotations, which shows a relatively high agreement. the second round of annotations, we ask the annotators to identify the protected groups being targeted, infringements of the most severe cases (Criminal Code s. 318, 319), and generate fine-grained labels by processing the target.
A.3 Additional Data Details
Positive class statistics by source forum are sum- marized in Table Most of the data sources have fewer examples of legal hate speech compared to their original labels except Gab, where our positive rate is greater.
htttss//ggituub..om//peeeaa

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Does statement hate speech laws?
The Indaas looed purchase their own land bailding. Canadians allow year olds lave temper fantrums that
inclict Canadiass
Meaning
Advocating 318)
incitement (3. 213)
Columbla, Human Rights (S. 7)
Rights (8.
Northweet Temrhories, Human Rights 13
Prohibited on META (FDVIG)
Proh bited on- Reddit
Prohibited on Twitter
Prohio led on YouTube
Please provide reasoning for your score, applicable.
(eg. This violates because this unclear because
RoTok-content includes the use non-sell referential skrr
ME content that decoribes-reppeeeeyy ta nett people with clurs
Figure 4:
The annotation interface shown legal experts. This shows example that annotated as hate speech, but the definition options only appear after selecting "Violates."
Legal source Prompt
Criminal Code, R.S.C. 1985, с C-46, s. 318
CC_318 is defined as Every person who advocates or promotes genocide is guilty of indictable offence and liable imprisonment for term of not more than five years, In this section, genocide means any of the following acts committed with intent to destroy in whole in part any identifiable group, namely, killing members of the group: or deliberately inflicting on group conditions of life calculated to bring about its physical destruction. An intent to directly prompt or provoke another person to commit genocide is enough to establish the mens rea of the offence.
Protected/iddetiffabbe groups include colour, race, religion, national ethnic origin, age, sex, sexual orientation, gender identity or expression, mental physical disability
Post: The Indians need to purchase their own land and building. Canadians do not allow 6 year olds to have the temper tantrums that Indians inflict on Canadians.
Q: Is this post violation of CC_318? Please answer yes no. A: <mask>
Table 7: An example of full prompt in the template. Each model uses this template.

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Criminal Code 8.318: Advocating Genocide
Age, Religion/Religious beliefs, Sex/Gender identity or expression, Race (colour, creed), Sexual orientation, Physical or mental disability, National or ethnic origin (nationality, ethnicity, ancestry)
Criminal Code s.319: Public Incitement of Hatred
Age, Religion/Religious beliefs, Sex/Gender identity or expression, Race (colour, creed). Sexual orientation, Physical or mental disability, National or ethnic origin (nationality, ethnicity, ancestry)
Human Rights Code. Alberta
Age, Religion/Religious beliefs, Source of income, Family status, Sex/Gender identity expression, Marital status, Race (colour, creed), Sexual orientation, Physical mental disability, National ethnic ori- gin (nationality, ethnicity, ancestry)
Human Rights Code, British Columbia
Age, Religion/Religious beliefs, Family status, Physical or mental dis- ability, Sex/Gender identity or expression, Race (colour, creed), Sexual orientation, Marital status, National or ethnic origin (nationality, ethnic- ity, ancestry)
Human Rights Code, Northwest Territories
Age, Religion/Rellgiouss beliefs, Family status, Family affiliation, Sex/Gender identity or expression, Conviction that subject to a par- don or record suspension, Marital status, Social condition, Race (colour, creed), Sexual orientation, Political belief/association, Physical or mental disability, National or ethnic origin (nationality, ethnicity, ancestry)
Human Rights Code, Saskatchewan
Age, Religion/Religious beliefs, Family status, Physical or mental dis- ability, Sex/Gender identity or expression, Race (colour, creed), Sexual orientation, Receipt of public assistance, Marital status, National or ethnic origin (nationality, ethnicity, ancestry)
Terms of Service, Meta
Religion/Rellgiouss beliefs, National ethnic origin (nationality, ethnic- ity, ancestry), Sex/Gender identity or expression, Serious disease, Race (colour, creed). Sexual orientation, Physical or mental disability, Famill affiliation
Terms of Service, Reddit
Religion/Religious beliefs, Sex/Gender identity expression, Victims of major violent event and their families/kin, Race (colour, creed), Sexual orientation, Immigration status, Physical or mental disability, National or ethnic origin (nationality, ethnicity, ancestry)
Terms of Service, Religion/Religious beliefs, National or ethnic origin (nationality, eth- nicity, ancestry), Physical or mental disability. Sex/Gender identity expression, Race (colour, creed). Sexual orientation, Immigration status, Serious disease, Family affiliation
TikTok
Terms of Service,
Age, National or ethnic origin (nationality, ethnicity, ancestry). Reli- gion/Religious beliefs, Sex/Gender identity or expression, Serious disease, Sexual orientation, Race (colour, creed), Physical or mental disability, Family affiliation
Twitter
Terms of Service,
Age, Religion/Religious beliefs, Family affiliation, Sex/Gender identity or expression, Victims of a major violent event and their families/kin, Race (colour, creed). Sexual orientation. Veteran status, Immigration status, Physical or mental disability, National or ethnic origin (nationality, eth- nicity, ancestry)
Youtube
Table 8: All definitions and their corresponding protected groups. Groups that are protected under all definitions are shown in bold.

---

Race (colour, creed)
actual and perceived race, colour, coloured, black, creed, racially, races, race perceived race ancestry, national, ethnic origin, ethnicity, place of origin, national origin, national or ethnic origin, tionality, indigenous identity
National ethnic origin (nation- ality, ethnicity, ancestry)
Political belief/association Sex/Gender identity or expres- sion
political association, political belief
gender, woman, men, transgender, boy, man, gender identity, gender identity and expression, gender iden- tity expression, sex, children, whore, sexist, baby, sex/gender
Religion/Rellgious beliefs
religion, cross, belief, christianity, judaism, faith, crosses, religions, nuns, religious affiliation, reli- gion/religious, religious beliefs
Sexual orientation
sexual orientation
Social condition
social condition
Immigration status
immigration status, immigration
Source income
source of income
Age
age, young. ages, old
Physical mental disability
physical mental disability, mental physical dis- ability, physical disability, mental disability, disabil- ity, impairment, disabilities, pregnancy or disability. adhd
Family affiliation
family affiliation, caste
Conviction that is subject to par- don record suspension
conviction that is subject to a pardon or record sus- pension
Receipt of public assistance
receipt of public assistance
Serious disease
serious disease, aids
Family status
family status pregnancy
Pregnancy
Victims of major violent event and their families/kin
victims of major violent event and their kin, victims of major event and their families
Veteran status
veteran status marital status
Marital status
Table 9: All search keywords used to identify target groups.


Reviews:
[{"review_id": "w71JGVEYU5", "reviewer": "Reviewer_9nW2", "summary": "", "questions": ".061\nThis subjectivity is not specific to computer sci-062\nence; hate speech is a highly debated topic, with ==> what do you mean by Computer science here?\n\n\nOur dataset contains samples from existing datasets 240\nwith the aim ...==> Which existing dataset?\n\nWe employ editing annotation tasks and data fil-246\ntering to increase the size of our gold data, while247\nutilizing experts to ==> This is not clear? what do you employ?\n\nIt is not clear how the SBIC dataset is labeled (positive??)\n\nHateXplain is not only from Twitter, but from Gab as well. Be precise in you related work ", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for taking the time to provide your feedback on our work! We will update the citation styles for the final version of the paper. Please find our response to your concerns below.\n\n## Reasons To Reject\n1. **How do we extend the approaches to other (countries' legal documents)?**\n\nFive of our definitions (Hateful Conduct Policies) are taken from social media companies, which can be applied internationally wherever these platforms are available.\n\nThis concern is valid for unseen criminal law and human rights codes in other countries, as cross-cultural notions of hate speech may vary. As noted in lines 223-231, nine of eleven definitions (including the five from social media companies) define very similar behaviours, to the point where we homogenize them in our second round of annotations. This might indicate our sampled definitions are culturally monotonous. We will add this discussion to the limitations section of our work, but we do not believe cultural bias detracts from the value of our study.\n\nWhile there is likely cultural bias in both the language models and our dataset, we argue that our zero-shot experiment results can also generalize to unseen definitions of hate speech. As seen in Table 7, we use unseen tokens for the target (CC_318, CC_319, etc.) to encourage models to rely on the prompted definitions. We will add this detail to our description of Table 2 in the main paper content, and we will release the full annotation document and the per-definition prompts with the dataset upon acceptance.\n\n2. **Data collection and annotation are not clear**\n\nWe made best effort to describe the data collection and annotation process in Section 3.1 (lines 240-317), and provide additional details in Appendix A. We also make note to refer to the appendix from the main text (lines 197, 276, 305).\n\n3. **The Enforceable Annotation might have ethical issues. What will be the reward for the 10 law experts? why did they volunteer? Does it count toward their study (credit), or will they co-author the paper? This is a serious issue, which might lead to the low quality of the data.**\n\nThank you for your careful feedback. We take ethical concerns seriously and we thank you for noticing we failed to specify annotator compensation.\n\nThe students were either rewarded with credits as part of a practicum course, or paid $15/hour, the minimum wage in our area, depending on the batch of annotations they participated in. For transparency, we did have one annotator that volunteered out of interest for the project, and they contributed <2% (<50/~2100) of the annotations, ~5 hours of work total (including meetings, training, and actual annotation work). We also ensured the data is high quality — as mentioned on line 997, the inter-annotator agreement is 0.623, indicating high agreement. We will add these details to Appendix A.2, line 970.\n\n4. **The concept of \"editing samples\" is not clear**\n\nPlease kindly refer to lines 334-340 for our description. “Editing” refers to adding or replacing words. To paraphrase the example we provide in line 339, “you are a jerk” is not hateful, but replacing “jerk” with a slur might be considered hateful.\n\n5. **Majority voting from 11 definitions is not clear?**\n\nPlease kindly refer to lines 301-305, as well as Appendix A.2, lines 983-986. We first take the coarse-grained label of Positive, Negative, or Unclear. If the sample is voted positive by a majority of 3 annotators, we take the or-wise combination of definitions/protected groups chosen by all annotators.\n\n6. **You could compare your result with SoTA approaches, for example with HateXplain models.**\n\nThank you for your comment. The main focus of our work is defining the task and establishing a baseline, so we believe it is not necessary to report state of the art approaches. In the HateXplain paper, they also report three general architectures: LSTMs, RNNs, and fine-tuned BERT.\n\nThat being said, we do report results on the current state of the art in instruction-tuned LLMs (GPT-4), and we use the LM-BFF method to enable prompting with an encoder-only model, RoBERTa-Large. While these approaches are not specific to hate speech, they achieve high performance on instruction-based tasks similar to our experimental setting.\n\n## Questions For The Authors\n1. **Clarifying line 61-62 reference to computer science**\n\nWe are referring to works in NLP and computational linguistics that seek to create automatic hate speech detection systems, in contrast with works in social sciences or law that debate the concept of hate speech; we will clarify this sentence in the final version of the paper.\n\n2. **Clarifying datasets, line 239-240**\n\nWe sample from all non-enforceable datasets listed in Table 1 and described in lines 251-283.\n\n3. **Clarifying lines 245-247, methods to increase gold data**\n\nWe employ the editing objective, lines 318-355, and automatic data selection, lines 356-387, to increase the size of the gold dataset.\nIt is not clear how the SBIC dataset is labeled (positive??)\n\n4. **Clarifying labels of SBIC**\n\n“Positive” refers to a positive sample of hate speech, following our naming convention of positive, negative, and unclear. As described in lines 262-265, we use a heuristic of offensive speech targeted at a group, both of which are existing annotations in the SBIC. Formally, we consider annotations where (offensiveYN > 0.5 && whoTarget == group) as the positive class, following previous work.\n\n5. **HateXplain description**\n\nThank you for the feedback. We acknowledge this mistake in our description, and we will update the text to say “Twitter and Gab” in the camera-ready version of our paper. Our dataset does include 13 HateXplain samples from Gab, as seen in Table 6 of Appendix A.3.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Extend hate speech with prompt templating\n- zero-shot classification for legally defined hate speech with many LLMs\n- Fine0tuned models for legally enforceable hate speech\n- Model result explanation", "reasons_to_reject": "- How do we extend the approaches to other (countries' legal documents)?\n- Data collection and annotation are not clear\n- The Enforceable Annotation might have ethical issues. What will be the reward for the 10 law experts? why did they volunteer? Does it count toward their study (credit), or will they co-author the paper? This is a serious issue, which might lead to the low quality of the data.\n- The concept of \"editing samples\" is not clear\n- Majority voting from 11 definitions is not clear? \n- You could compare your result with SoTA approaches, for example with HateXplain models.", "questions_to_authors": ".061\nThis subjectivity is not specific to computer sci-062\nence; hate speech is a highly debated topic, with ==> what do you mean by Computer science here?\n\n\nOur dataset contains samples from existing datasets 240\nwith the aim ...==> Which existing dataset?\n\nWe employ editing annotation tasks and data fil-246\ntering to increase the size of our gold data, while247\nutilizing experts to ==> This is not clear? what do you employ?\n\nIt is not clear how the SBIC dataset is labeled (positive??)\n\nHateXplain is not only from Twitter, but from Gab as well. Be precise in you related work ", "paper_topic_and_main_contributions": "This paper experiments with employing LLMs for legally enforceable hate speech. They have used existing datasets and incorporated definitions from current laws, in Canada. They have experimented with prompt engineering and fine-tuning LLM with existing data. They have also produced gold and silver datasets.\n\nOne big issue with this work is that the annotation/annotator strategy is not clear. If annotators are not rewarded for their task, this might raise an ethical issue.  ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Justification_for_Ethical_Concerns": "No clear indication about annotator rewards"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "search introduced in (Khurana 133\net al., 2022; Zufall et al., 2022)  ==> search introduced in Khurana 133et al., (2022); Zufall et al., (2022) \n\nshot methods reduce requirements for training 142\ndata AlKhamissi et al. (2022). ==> ...Please correct the citation styles, it is very random. This should be as .... shot methods reduce requirements for training data (AlKhamissi et al. 2022).\n\n"}}, {"review_id": "2c29qtLCmm", "reviewer": "Reviewer_Ukeu", "summary": "", "questions": "N/A", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your feedback on our work! Please find our response below.\n\n## Reasons To Reject\n\n**The paper primarily focuses on aligning hate speech detection with legal definitions. It may lack a broader analysis of other aspects related to hate speech and its impact.**\n\nWe argue that legal definitions have been created by policy makers and legal professionals who have analyzed and seek to mitigate the societal impacts of hate speech, so our work is already a direct assessment of hate speech and its impact. Previous works might capture various aspects of hate speech, but as discussed in lines 51-74, their definitions are often incomplete or inconsistent with one another. \nIn general, we observed that existing works can often conflate concepts with hate speech that are distinct in the law, such as discrimination. As seen in Table 1 and discussed in lines 310-317, works that target more “subtle” definitions such as Implicit Hate Speech had all of their positive samples flipped to negative because of this conflation. For more details on differences between existing works and legal definitions, please refer to our rebuttal to reviewer G4KL.\n\nWe believe that the community should shift their approach to align with existing, accepted policies of high impact tasks. This guides future research towards the interests of society and creates opportunities for applications and inter-disciplinary collaboration.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper introduces a unique perspective on hate speech detection, which focuses on grounding the task in legally enforceable definitions. \n2. The release of a gold label dataset annotated by legal experts is a substantial contribution to the research community. Such a dataset can serve as a reliable benchmark for evaluating and comparing hate speech detection systems. \n", "reasons_to_reject": "The paper primarily focuses on aligning hate speech detection with legal definitions. It may lack a broader analysis of other aspects related to hate speech and its impact. ", "questions_to_authors": "N/A", "paper_topic_and_main_contributions": "The authors introduce a novel approach to hate speech detection by grounding the task in enforceable legal definitions. The authors present a dataset annotated by legal experts, containing violations of eleven possible definitions of hate speech. To address the challenge of identifying clear, legally enforceable instances of hate speech, the dataset is augmented with expert-generated samples. They report results on several large language models (LLMs) and demonstrate that with this task definition, hate speech detection can be more closely aligned with enforceable laws, thus enabling more rigorous enforcement of legal protections against harmful speech in public forums. \n", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "6ML4dihyPQ", "reviewer": "Reviewer_G4KL", "summary": "", "questions": "* A: Hate speech definitions, including legal ones, evolve over time. I'm\nwondering how well will this dataset stand the test of time? Is there a simple\nway of keeping the dataset up to date instead of re-annotation by legal experts?\n* B: Although Table 1 contains a comparison of the number of positive samples in\nthe original and the relabeled corpora, it would be interesting to discuss the\ndifferences of the original definitions and the ones used in this work in more\ndetails. What are the main differences of definitions other than the use of more\nsubtle forms of hate speech as mentioned in line 314?\n* C: What is the degree of the annotators' agreement?\n", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for taking the time to review our work! We will update your presentation improvements in the camera ready version of the paper. Please find our response to your questions below.\n\n## Questions For The Authors\n**A: Hate speech definitions, including legal ones, evolve over time. I'm wondering how well will this dataset stand the test of time? Is there a simple way of keeping the dataset up to date instead of re-annotation by legal experts?**\n\nThis is a valid concern for any hate speech dataset, and a primary motivation of this work. One advantage of using legal definitions is that they are normally passed after a rigorous evaluation process. They can be overturned or amended one day, but they have already been validated by many experts, and are relatively slow to change compared to previous works. We also analyze all 11 definitions and find that nine of them are relatively similar, as discussed in lines 223-231, so this dataset is in line with current western ideas of hate speech.\nWe argue that our zero-shot experiments also show performance on unseen definitions of hate speech. We use unseen tokens (CC_318, CC_319, etc.) to encourage models to rely on the prompted definitions.\n\n**B: Although Table 1 contains a comparison of the number of positive samples in the original and the relabeled corpora, it would be interesting to discuss the differences of the original definitions and the ones used in this work in more details. What are the main differences of definitions other than the use of more subtle forms of hate speech as mentioned in line 314?**\n\nBy “subtle” we refer to micro-aggressions and implicit statements, which are the main focus of Implicit Hate Speech and CounterContext. Actually, we argue these works are not aligned to the legal concept of hate speech, instead capturing the broader concept of _discrimination_ (prejudice towards certain groups of people). We internally discussed the enforceability of discrimination versus hate speech at great lengths before removing discrimination policies from our definition. Discrimination is a broad concept and its impact on society is varied, making the legal implications not well defined.\n\n(!!!!! WARNING: OFFENSIVE LANGUAGE)\nFor example, here is one (cherry-picked) label-flipped sample from Implicit Hate Speech : \n> “this is crazy. how do you give a loan to someone who can be deported at will ?”\n\nThe speaker believes “someone who can be deported at will” (immigrants) should not be able to get loans, which is discrimination but not hate speech. Hate speech in the law is defined as directly insulting/dehumanizing/humiliating someone based on a protected attribute. Multiple definitions reference, for example, comparing a group of people to insects.\n\nThis might be closer to hate speech: \n> \"they shouldn't be giving out loans to someone who can be deported at will. those cockroaches can’t be trusted\"\n\nThis is also a main motivation of our work — because previous works do not reference the law, they can conflate different legal concepts, and our understanding of hate speech in the NLP community becomes misaligned with experts in other disciplines.\n\nThank you for the question. We will add this discussion to the paragraph at line 314 in the camera ready version of the paper.\n\n**C: What is the degree of the annotators' agreement?**\n\nThe Krippendorff’s Alpha is 0.623, which indicates high agreement. This is mentioned on line 997 (Appendix A.2).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* The built dataset will be useful for researchers to evaluate classifiers\nfollowing hate speech definitions based on legally enforceable definitions.\n* The paper also presents a useful benchmark for comparison of future systems,\nwhich includes various models, model setups (zero-shot, tuning) and evaluation\nsetups. Additionally, the presented experiments provide a useful analysis on the\nperformance and behaviour of LLMs on legally enforceable hate speech detection.\n* The writing of the paper is high quality and detailed.\n", "reasons_to_reject": "* I did not find any significant reasons for rejecting the paper. Please see\nbelow for some of my concerns/issues.\n", "questions_to_authors": "* A: Hate speech definitions, including legal ones, evolve over time. I'm\nwondering how well will this dataset stand the test of time? Is there a simple\nway of keeping the dataset up to date instead of re-annotation by legal experts?\n* B: Although Table 1 contains a comparison of the number of positive samples in\nthe original and the relabeled corpora, it would be interesting to discuss the\ndifferences of the original definitions and the ones used in this work in more\ndetails. What are the main differences of definitions other than the use of more\nsubtle forms of hate speech as mentioned in line 314?\n* C: What is the degree of the annotators' agreement?\n", "paper_topic_and_main_contributions": "The paper introduces a dataset for hate speech detection, labeled based on legal\ndefinitions, and tests LLMs to assess their classification quality on them. A\nfrequent problem with hate speech datasets is the hate speech definition\ndifference across various corpora leading to various issues, such as\ndifficulties in enforcing the outputs of classifiers legally which the paper\naims to improve.  The dataset is composed of previously introduced datasets,\nhowever, the input samples are relabeled by legal experts based on various legally\nenforceable hate speech definitions, such as on federal laws. The authors\nevaluated various LLMs on the database, including coarse- and fine-grained\nclassification setups, as well as the evaluation of model explanations,\nserving as a benchmark for future research on the dataset.\n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "* The description of the definitions in Table 8 is not presented. However, I\nwould suggest including them as well for more clarity.\n* The description of WizardLM seems to be missing.\n* There is a mismatch in the title between the pdf and Openreview.\n"}}]

Author rebuttal:

Meta-review: The paper focusses on legally enforceable hate speech detection.
The most important contributions is the release of a dataset annotated by legal experts.
The paper also presents a useful benchmark for comparison of future systems, which includes various models, model setups (zero-shot, tuning) and evaluation setups. 

The criticism that the paper "lacks a broader analysis of other aspects related to hate speech and its impact." is somewhat irrelevant to the main contribution it is making.
Decision: Accept-Findings