## 1 Introduction  
The rapid expansion of online communication platforms has significantly intensified exposure to various forms of harmful content, including hate speech, posing considerable challenges to digital well-being and effective content moderation. At the same time, rapid advances in large language models (LLMs) have revolutionized the processing, understanding, and generation of human-like text, leading to its increasing integration into systems that profoundly affect our social interactions [1]. Despite their remarkable success, a critical concern is that these models may learn, perpetuate, and even amplify detrimental social biases present in their training data. Addressing this problem requires rigorous research focused on measuring and mitigating social bias to ensure fairness in natural language processing (NLP) systems. However, this effort fundamentally requires precise definitions of the various types of social biases that can arise from LLMs, going beyond general \"safety\" concepts that may lack explicit definitions [2; 3; 4]. A common flaw in current research on bias and fairness in LLMs is insufficient precision in describing the specific harms caused by model behaviors [5; 6; 7]. This includes failing to clearly articulate who is harmed, the underlying reasons for harmful behavior, and how such harm reflects and reinforces existing social principles or power hierarchies. This challenge is especially acute when it comes to toxicity and offensive language, which are intrinsically intertwined with historical and structural power asymmetries. Moreover, implicit forms of toxicity, such as coded language, sarcasm, and micro-aggressions, remain notoriously difficult to detect, and very few existing corpora provide reliable and consistent annotation of such subtleties [5; 8]. Specifically naming these harms, understanding their origins in social relations and histories, and recognizing the assumptions made in their conceptualization are crucial steps to effectively defining the role of NLP technologies in reproducing inequity and injustice [9; 10]. In recent years, there has been considerable expansion in the development of annotated Spanish-language datasets specifically aimed at identifying toxicity, hate speech, and aggression. Previous studies, such as that of [PERSON] et al. [11], have systematically categorized these existing resources, detailing their characteristics, annotation schemes, and main limitations. However, while contemporary corpora like contextualized hate speech [12], NewsCom-TOX [11], and DETESTS-Dis [13] incorporate context, their contextual depth is predominantly monological or limited to short discussion threads. Moreover, it is imperative to expand the scope of bias and fairness considerations beyond the typically assumed American and Western contexts and to languages other than English, necessitating the creation of additional linguistic resources that accurately reflect the diverse linguistic features and representations of bias [1]. While the creation of annotated Spanish-language datasets has progressed in recent years, significant gaps remain in capturing both rich dialogical context and cultural diversity in expressions of bias and fairness. To design future linguistic resources that overcome these limitations, several strategies can be adopted, including cross-cultural sampling ensuring representation of local slang, references, and socio-political contexts that shape the manifestation of bias, incorporate dialogues from multiple platforms, and involve annotators from multiple cultural backgrounds providing guidelines that explicitly address region-specific stereotypes, taboo language, and forms of humor or sarcasm, thus improving annotation reliability for implicit and culturally nuanced cases. Another important aspect is to consider conversational windows that consider multi-turn dialogues, enabling models to leverage pragmatic cues, and subtle forms of context-dependent toxicity that are lost in single turns.