D ia S afety- CC : Annotating Dialogues with Safety Labels and Reasons for Cross-Cultural Analysis (2025)

Abstract: A dialogue dataset developed in a language can have diverse safety annotations when presented to raters from different cultures. What is considered acceptable in one culture can be perceived as offensive in another culture. Cultural differences in dialogue safety annotation is yet to be fully explored. In this work, we use the geopolitical entity, Country, as our base for cultural study. We extend DiaSafety, an existing English dialogue safety dataset that was originally annotated by raters from Western culture, to create a new dataset, DiaSafety-CC. In our work, three raters each from Nigeria and India reannotate the DiaSafety dataset and provide reasons for their choice of labels. We perform pairwise comparisons of the annotations across the cultures studied. Furthermore, we compare the representative labels of each rater group to that of an existing large language model (LLM). Due to the subjectivity of the dialogue annotation task, 32.6% of the considered dialogues achieve unanimous annotation consensus across the labels of DiaSafety and the six raters. In our analyses, we observe that the Unauthorized Expertise and Biased Opinion categories have dialogues with the highest label disagreement ratio across the cultures studied. On manual inspection of the reasons provided for the choice of labels, we observe that raters across the cultures in DiaSafety-CC are sensitive to dialogues directed at target groups compared to dialogues directed at individuals. We also observe that GPT-4o annotation shows a more positive agreement with DiaSafety labels in terms of F1 score and phi coefficient.

## introduction neural conversational ai models that engage in open-domain dialogues or chit-chats can converse with an interlocutor about varieties of topics, spanning multiple domains @xcite . opendomain chatbots have gained popularity with the rapid development of large language models (llms). despite the conversational capabilities of these models, they struggle with demonstrating cultural awareness in some scenarios, thereby raising concern about how safe it is to interact with these models. safety issues in a conversational ai model manifest in the form of a model generating (or responding inappropriately to) harmful contents or providing false impression of its nature or capability @xcite . existing datasets for training neural conversational ai models and harmful dialogue detection models are largely developed in high-resourced languages or curated by participants from western culture @xcite @xcite . when users from non-western cultures interact with these models, they find the lack of cultural awareness of these models insensitive or toxic @xcite . given a context, an utterance rated as non-toxic by an individual from a culture could be perceived as toxic by an individual from another culture @xcite . cultural disparities in the annotation of dialogues for safety evaluations is under explored. in this work, we aim at investigating how the country of raters influence annotations of dialogue datasets for safety considerations. we hypothesise that given the same dialogue, there would be differences in the safety annotation by raters from different cultures. instead of race, we use country as our basis of cultural alignment study, considering individuals from the same country share more similar cultural norms and values compared to people from the same race. we pose the questions: (q1) to what extent do raters from different countries disagree on safety annotations given the same dialogues? (q2) to what extent do raters from the same country disagree on safety annotations given the same dialogues? (q3) which categories have the most disagreements on dialogue safety annotations among the different cultures studied? in order to answer our questions, we leverage di-asafety @xcite , an existing dialogue safety dataset annotated by native english speakers. we reannotate the dataset by engaging participants from nigeria and india, as shown in figure 1 . specifically, our contributions are highlighted as follows: ## cultural awareness in dialogue safety annotation culture, which is generally seen as the totality of the way of life of people @xcite has been studied long before now. culture encompasses a wide range of human activities and traits, including knowledge, beliefs, customs and morals @xcite . this broad definition highlights the intricate role of culture in shaping behaviours, particularly in the context of dialogue annotation across diverse societies. there has also been prior work that considers culture from an anthropological perspective in terms of actions, things and concepts viewed in the context of other actions and things @xcite . from a historical and subjective standpoint, culture can be understood as the collaborative construction of membership within a discourse community. such a community is characterised by shared social spaces, histories and collective imaginings. even when individuals depart from this community, they may continue to carry a shared framework of norms and standards that influence their perception, beliefs, judgements and actions @xcite . researchers are also interested in how culture plays a role in technical systems, especially how these systems perform when exposed to different cultures of the people who use them. cultural alignment involves tailoring an ai system to correspond with the collective beliefs, values and norms of the user group that engages with the system @xcite . cross-cultural research, which involves studying the differences across cultures has been gaining attention lately, especially with the rapid development of llms. an area where cross-culture is yet to be fully explored is annotation of dialogue datasets for safety evaluations, where an ideal diverse rater pool would consist of participants of different demographic characteristics providing ratings for dialogues. most of the available dialogue datasets are developed by participants from western countries @xcite . cross-cultural dialogue annotation for safety evaluation takes into account cultural nuances, which significantly influences how we communicate safety-critical information. cross-cultural annotation in conversational ai systems is crucial in helping to identify potential disagreements, misunderstandings or biases that could arise due to cultural differences. incorporating diverse perspectives in dialogue help to create systems that are inclusive, reliable and effective in addressing safety concerns across various cultural settings @xcite . ## related work there has been existing work involving humans providing and annotating dialogues for safety evaluations @xcite @xcite . prior work has also been conducted with annotators providing dialogue safety labels and generating safer responses to problematic examples according to commonsense social rules @xcite . an important area of dialogue research that has been gaining attention lately is developing dialogue safety datasets with raters providing rationales for their choice of labels. @xcite released the dices (diversity in conversational ai evaluation for safety) dataset, with the aim to address the need for diverse perspectives in evaluating the safety of conversational ai systems. the authors collected multi-turn adversarial conversations of humans interacting with a dialogue model. the datasets: dices-990 was rated by participants from the us and india, while dices-350 was rated by participants from us only. the dataset includes detailed demographic information about raters: gender, age, geographic location and race. @xcite proposed crehate, a crosscultural english hate speech dataset. the authors sampled posts from sbic dataset, which largely represents north america. the authors conducted annotations on the collected posts with participants from four countries (australia, united kingdom, singapore and south africa) and the united states. the authors found out that 56.2% of crehate achieve consensus annotations from the selected countries with 26% pairwise label difference rate. their qualitative analysis highlights label disagreements result from annotators' differing perspectives of what constitutes sarcasm and personal bias on divisive topics. researchers have studied how llms align with human raters. @xcite investigate the alignment of safety perceptions in humans and llms. the authors re-annotate the dices dataset, using five models, to study the extent to which humans and llms agree when annotating dialogues. the authors observed that larger datasets (than the 350 dialogues in dices) are needed to resolve whether gpt-4 shows disparities in correlation with different demographic groups. also, compared to the average annotator rating, the authors found out that gpt-4 achieves a pearson correlation, @xmath0.59 and averagely, @xmath1.51 of the median annotator's correlation. the importance of considering cultural alignment when deploying llms and a discussion of their performance across diverse cultural contexts is emphasised in the work of @xcite . the authors proposed using the cultural alignment test (cat) to quantify cultural alignment in llms. in order to conduct cross-cultural comparison, the authors leverage hofstede's cultural dimensions as a framework. the authors learned that the considered llms did not perform satisfactorily in understanding cultural values across all tested countries. for the cultural values of the united states, gpt-4 exhibited the highest cat score. similar to @xcite , we extend an existing dialogue safety dataset by asking raters to annotate the dialogues with safety labels and provide reasons for their choice of labels as free-form text. instead of race, we conduct cross-cultural analyses of the annotations with a focus on the country of the participants. we also conduct a comparative study of the annotation differences of the rater groups to the existing annotation (provided by participants from a different culture) of the original dataset. in our evaluation, we also compare llm annotation @xcite to the representative labels of each rater group. ## methodology in this section, we discuss the procedures we adopt in carrying out our research in this section. ## experimental setup we provide information on the resources that aid our experiments in this section. ## results and discussion we discuss the outcome of our experiments and findings in this section. ## conclusion in this work, we extend an existing english dialogue safety dataset. raters from non-western cultures, who communicate in english at all educational levels, reannotate the dialogues in the test set and provide reasons for the label choices. we found out that there are differences in the raters' labels in our work compared to the labels in the original dataset. also, gpt-4o labels align more with labels in the original dataset. in our findings, we observe that raters disagree the most on unauthorised medical conversations and dialogues perceived to project biased opinions. our qualitative analysis shows that raters across the non-western cultures studied are more sensitive to dialogues directed at target groups than dialogues directed at individuals.