Exploring the Merchera Ethnic Group Through ChatGPT: The Risks of Epistemic Exclusion

Abstract

The study of underrepresented ethnic groups in the social sciences is often hindered by structural and epistemic barriers that limit access to culturally embedded knowledge. This article examines the potential of the GPT-4 version of the ChatGPT language model as a complementary research tool used to generate insights into the Merchera ethnic group, whose presence in the academic literature remains minimal and often characterised by misrepresentation. Through a comparative analysis considering ChatGPT responses and the scarce number of existing sources, this study explores the model’s reliability, depth, and limitations. The findings reveal that while ChatGPT offers a coherent synthesis of available knowledge, it tends to reproduce the prevailing biases and informational gaps found in the existing academic discourse. The paper concludes that generative AI may serve as a provisional support for research on marginalised communities, but its outputs must be interpreted with caution and situated within a framework of critical inquiry and ethical responsibility.

1. Introduction

Advances in artificial intelligence (AI) are significantly transforming contemporary techno-economic paradigms, generating extensive debate regarding the societal impacts of these advances across a wide range of sectors—from healthcare to education [1,2,3,4]. Among these technological developments, generative AI stands out for its capacity to autonomously produce original and contextually appropriate content. This innovation raises critical questions about the implications of such tools for the rigour, accuracy, and integrity of knowledge production [5,6]. A prominent example in this domain is ChatGPT, a language model based on deep learning that has demonstrated remarkable capabilities in generating coherent natural language and simulating human-like dialogue [7].

In the context of scientific research, ChatGPT has emerged as a powerful tool for processing and synthesising large volumes of work within the academic literature. Its capacity to detect patterns, identify thematic trends, and suggest new lines of inquiry can assist researchers in navigating increasingly complex knowledge landscapes [5]. By automating aspects of data analysis, ChatGPT contributes to enhanced research efficiency and accessibility. However, this accessibility also prompts important concerns regarding the depth, reliability, and critical insight of the knowledge it produces [8].

Given that ChatGPT is trained on extensive corpora of existing texts—many of which may contain embedded biases—there is a real risk that such biases are replicated and reinforced by the model. This may, in turn, influence research agendas and scholarly discourses in subtle but consequential ways [5]. The application of AI in knowledge generation must therefore be situated within broader socio-cultural and ethical principles—such as gender equity, cultural sensitivity, the protection of marginalised communities, and environmental sustainability—which must be integrated into the design and development of AI tools like ChatGPT [9,10].

These intersecting concerns highlight the need for critical engagement with generative AI to ensure that its use not only increases research productivity but also supports responsible and inclusive knowledge production. Accordingly, the implementation of AI in academic research must be guided by ethical imperatives that promote human security, cultural understanding, and social progress [3].

Against this backdrop, the present study focuses on a historically marginalised and under-researched group: the Merchera ethnic community. Traditionally characterised by a seminomadic lifestyle and a distinct sociocultural worldview, the Mercheros have received scant attention in the social sciences and are often subject to persistent misrepresentations and stereotypes [11]. The emergence of generative AI offers a novel methodological opportunity to explore existing knowledge on the Merchera community using ChatGPT as a research tool.

This study investigates both the utility and the limitations of ChatGPT in generating insights into the Merchera group. In doing so, it also reflects on the broader epistemological and ethical implications of employing generative AI in cultural and social research contexts [12].

The analysis is informed by the existing scholarship on epistemic injustice and exclusion, particularly within the context of systemic marginalisation in knowledge production and the imperative to promote epistemic justice [13]. These conceptual frameworks, which emphasise the structural barriers to equitable participation in the production of knowledge, are especially relevant in light of the potential for AI technologies to reproduce or reinforce existing biases [14]. Within this context, the present study positions the case of the Merchera within broader discussions of epistemic equity and cultural recognition, factors which underscore the importance of the valuing of diverse epistemologies as a foundation for inclusive and socially responsive research practices [15].

Recent contributions to the literature further stress the ethical and humanistic challenges associated with the deployment of AI in academic and educational contexts, highlighting epistemic exclusion as a key area of concern [16]. While generative AI holds considerable promise for the enhancement of access to information and the expansion of the scope of academic inquiry, its use raises fundamental ethical questions concerning transparency, fairness, and representational accuracy. In particular, there is a growing recognition that ethical frameworks for AI remain underdeveloped. Recent research highlights a lack of clarity regarding the ethical principles that should govern the design, development, and deployment of trustworthy AI in education [17] (p. 4222). This normative gap is especially salient when AI is applied to contexts involving historically marginalised or misrepresented communities.

Furthermore, the influence of generative AI extends beyond epistemic domains to moral reasoning itself. There is emerging evidence that such technologies can “alter moral beliefs and practices” [18] (p. 1), thus complicating their integration into sensitive areas of cultural and social research. Of particular concern is the presence of what has been termed an “inverse skills bias,” wherein generative AI systems appear to favour users with lower actual or perceived cognitive ability [18]. Although this tendency may initially appear to promote access, it also challenges prevailing understandings of equality of opportunity and may contribute to shifts in how such norms are interpreted or prioritised over time.

If, as some scholars suggest, existing commitments to equal opportunity are to remain meaningful, deliberate efforts must be undertaken to ensure equitable access to generative AI technologies [18]. This is particularly relevant for communities like the Mercheros, whose cultural and epistemic contributions have historically been marginalised or ignored. By situating this case study within broader debates on AI ethics and epistemic justice, the present research seeks to contribute to an emerging body of critical scholarship advocating for the responsible, inclusive, and context-sensitive deployment of AI in the social sciences.

2. The Merchera Ethnic Group: Context and Epistemic Challenges

2.1. Origins of the Merchera Community

The historical and cultural relevance of the Merchera ethnic group is fundamental to understanding the context in which this study is situated. The Mercheros—also referred to as Caló gypsies of the steppes—are a nomadic Romani group with a historical presence in various regions of Spain, Portugal, and France [19]. Their distinctive traditions and ways of life make them a unique and valuable community for scholarly inquiry.

Despite the significance of the Merchera community, access to reliable information remains severely limited. The few available academic sources often provide contradictory accounts of the group’s origins, identity, and even their classification within the broader Romani community. This lack of coherent scholarship has contributed to the community’s persistent underrepresentation in research, a condition further compounded by social stigma and the challenges associated with accessing such a marginalised group.

The origins of the Merchera community remain a subject of ongoing debate, with some scholars arguing for their Romani heritage, while others suggest a more complex sociohistorical formation shaped by intersecting cultural influences [11,20,21,22].

These gaps in the literature reflect broader patterns observed in Romani studies, where researchers have consistently encountered obstacles in building sustainable, inclusive research relationships. Ethnographic studies have documented the complexity of engaging with Romani groups whose lifestyles and internal diversity often challenge the construction of unified research communities [23]. Moreover, there is growing concern over the extent to which educational and social interventions aimed at supporting Roma populations reach the most marginalised individuals, as opposed to benefiting more integrated or socioeconomically advantaged subgroups [24].

Access to education emerges as a recurrent theme across European Romani research. In countries such as Greece, Bulgaria, and the United Kingdom, Roma students face systemic discrimination, institutional racism, and exclusion from formal learning environments [25,26,27]. Nevertheless, education is also framed as a site of transformation and agency, particularly for Roma women, who have identified learning as a key strategy for overcoming structural inequalities and challenging traditional gender roles [28]. These findings are relevant to the present study, which explores how AI-generated representations may either reproduce these patterns of exclusion or offer new opportunities for visibility and self-representation.

Language and identity are also central concerns in the scholarship on Roma communities. In the Czech Republic, debates over Romani language preservation highlight the tension between state-driven standardisation efforts and community-led practices grounded in vernacular use [29]. Such discussions underscore the need to move beyond top-down frameworks by adopting transdisciplinary, participatory methodologies that engage community members as co-researchers in the construction of knowledge [30].

While mainstream discourse often portrays Roma groups through a deficit lens—associating them with poverty, marginalisation, and social conflict—research also points to strong cultural knowledge systems, including informal and situated learning practices [31]. These insights align with broader frameworks of epistemic justice [15] that advocate for the recognition of diverse knowledge traditions that are frequently excluded from institutional settings.

The case of the Mercheros thus reflects a wider pattern of epistemic exclusion within Romani research, in which underrepresentation is both a symptom and a driver of social marginalisation. While the emergence of tools such as ChatGPT may offer new avenues for exploring the history and identity of this community, their application must be critically assessed. These technologies should be employed not as substitutes for rigorous research, but as complementary tools, taking into account that their output requires validation against the established literature, and facilitating the identification of any underlying sources of bias or inaccuracies [32].

2.2. Cultural Values and Identity

As with any ethnic or cultural group, values and practices may vary among individuals and subgroups within the Merchera community. Nevertheless, the Mercheros are broadly characterised as “a traditionally nomadic human group of uncertain origin, united by a shared ideology, ethical code, values, customs, traditions, dialect, physical traits, and a distinct character, all of which have been preserved over several centuries” [33].

One of the key challenges in studying the Merchera community lies in the scarcity of academically rigorous sources. This is largely due to the oral cultural tradition of the community, which limits the availability of written documentation. While blogs are not conventional academic references, the inclusion of such sources [33] is considered appropriate in this context, given the community’s marginalization and the limited scope of formal research. According to this reference source, the Merchera ethical code—or honor system—is built on a strong set of shared values and unwritten rules, as illustrated in Figure 1. These principles form the foundation of social cohesion and identity within the group.

It is particularly important to note that, despite being historically stigmatised and subjected to persistent negative stereotypes, the ethical code of the Mercheros promotes values that directly counter these misconceptions. Rather than adhering strictly to either deontological or teleological ethical systems, their code might be more accurately described as a community-based moral framework rooted in lived experience and cultural continuity [34].

This aligns with broader findings in Romani studies, which highlight the centrality of ethical codes, oral knowledge transmission, and intra-community regulation in the maintenance of cultural identity among nomadic or semi-nomadic groups. For instance, research on Romani communities has similarly shown how social norms and honour systems operate outside formal institutional structures, often resisting assimilationist pressures, while fostering internal cohesion and resilience [23,35]. Such comparative insights reinforce the importance of recognising the Merchera community not through the lens of deficiency or deviance, but through the richness of its cultural practices and ethical worldview.

2.3. Understanding Epistemic Exclusion and Epistemic Injustice

The concept of epistemic exclusion provides a critical framework for understanding the systemic silencing and marginalisation of certain knowledge systems, particularly those produced by historically underrepresented or minoritised communities. Within academic and scientific contexts, epistemic exclusion refers to the institutional and interpersonal processes through which the knowledge of non-dominant groups is devalued, misrepresented, or omitted entirely from scholarly discourse [36,37,38,39].

This exclusion operates across multiple levels. Structurally, formal academic systems often prioritise dominant epistemologies, marginalising alternative “ways of knowing”, such as oral histories, experiential knowledge, or community-based understandings [40,41]. These mechanisms reinforce the hegemonies of particular worldviews, languages, and methodologies, systematically disadvantaging those who do not conform to dominant academic standards or modes of inquiry [42].

Importantly, such exclusion is not limited to a lack of material resources. Rather, it reflects deeper issues of misrecognition—a failure to acknowledge the legitimacy of diverse cultural expressions, histories, and epistemologies [43]. This constitutes a form of symbolic violence that restricts epistemic agency and perpetuates social hierarchies, ultimately impacting the capacity of marginalised groups to participate in the production of knowledge [36].

This theoretical framework intersects directly with the concept of epistemic injustice, as introduced by philosopher Miranda Fricker. Fricker defines epistemic injustice as a “wrong done to someone specifically in their capacity as a knower” [44] (p. 51). She distinguishes between two primary forms: testimonial injustice, which occurs when identity-based prejudice leads a hearer to assign reduced credibility to a speaker’s word, and hermeneutical injustice, which arises when marginalised groups lack access to the interpretive resources needed to make sense of their social experiences [44].

These dynamics are particularly relevant to the Merchera community, whose historical narratives and cultural identities have been largely ignored or distorted in the academic literature. The absence of rigorous scholarly research not only reflects informational scarcity but also reveals deeper epistemological biases. As Vaditya [41] notes, dominant groups often succeed in presenting their epistemic framework as the “natural” or most legitimate lens through which the world is understood. Consequently, the lived experiences and values of communities like the Mercheros are frequently rendered epistemically inferior.

Even when individuals from marginalised backgrounds gain entry into academic spaces, they are often subject to tokenism—a phenomenon that exacerbates epistemic exclusion by making their presence hypervisible while simultaneously rendering their contributions invisible or undervalued [45]. Thus, epistemic exclusion is not merely an issue of underrepresentation. It is deeply embedded in how knowledge itself is defined, validated, and circulated. It raises urgent questions about who is allowed to produce knowledge, whose experiences are considered legitimate data, and which methodologies are deemed scientifically acceptable [46]. As Kay et al. [47] argue, without access to shared conceptual resources, the experiences of oppressed groups can remain unintelligible within dominant discourses. These groups are excluded not only from knowledge production, but also from collective understanding.

Emerging scholarship has begun to address how these injustices also manifest in digital environments, particularly in relation to AI and algorithmic systems. Although much of the existing literature on epistemic injustice has focused on human interactions, recent studies have highlighted the growing role of AI technologies in shaping knowledge ecosystems [44,47]. Since AI systems are epistemic technologies—they consume, curate, and produce information—they can both reflect and reinforce existing epistemic injustices [48].

This concern is at the heart of the newly minted field of algorithmic epistemic injustice, a field which examines how machine learning systems may replicate identity-based prejudices with respect to how these systems process and generate knowledge [49]. Far from being neutral tools, these systems often encode the very biases that exclude marginalized voices from academic and public discourses.

In the case of the Merchera community, this framework underscores the challenges involved in retrieving reliable information and highlights the importance of methodological innovation. Although technologies like ChatGPT may offer opportunities to uncover perspectives often overlooked in mainstream discourse, their results require validation against the existing literature and careful attention to embedded biases [32].

3. Methodology

This section details the ChatGPT model employed in the study and the procedures followed for data collection and selection regarding the Merchera ethnic group. For the purposes of this research, data were gathered through a series of structured interactions with the model, each designed to elicit information on different aspects of the Merchera community. These included inquiries into their historical background, cultural practices, genetic characteristics, and social organisation.

3.1. Study Design

Prior to engaging with ChatGPT, a scoping review of the existing literature on the Merchera ethnic group was conducted across multiple academic databases, including Google Scholar, Dialnet, CEPAL, DANE, SciELO, and Redalyc. The search strategy —outlined in

Table 1. Search terms used in the literature review across languages.

Language	Search Terms
Spanish	“Etnia merchera”, “merchera”, “mercheros”. 1
English	“Merchera ethnic group”, “Merchera”, “Mercheros”. 1
French	“Groupe ethnique merchera”, “Merchera”, “Mercheros”. 1

¹ Boolean combinations: AND, OR, NOT.

—utilised the following keywords in Spanish: “Etnia merchera”, “merchera”, and “mercheros”. These terms were combined using Boolean operators (AND, OR, NOT) with related descriptors such as “gitano”, “gitanos”, and “gitanas”—the Spanish masculine and feminine forms of the term “Gypsy.”

The review adopted an inclusive search strategy, incorporating all documents containing the root words “merchera” or “merchero” in any language, without imposing temporal or linguistic restrictions. To enhance coverage, keywords were translated and expanded—“merchera ethnic group” in English and “groupe ethnique merchera” in French. Nevertheless, three exclusion criteria were applied to maintain academic rigour: (1) lack of sufficient scholarly quality, (2) irrelevance to the research focus—particularly in cases where the Mercheros were not explicitly addressed, and (3) unavailability of the full text.

In addition, supplementary research was undertaken in libraries and archival collections. However, the majority of available materials centred on the broader Romani community, rather than specifically addressing the Mercheros. Owing to this limitation, it was necessary to procure two key reference works on the Merchera community [20,21], works which were not accessible through the existing library network.

3.2. Application of the ChatGPT Model

For the purposes of this study, the GPT-4 version of the ChatGPT language model—released in 2024—was employed. Data collection consisted of engaging the model in a series of structured dialogues, guided by carefully formulated prompts. These prompts were designed to elicit comprehensive information on various dimensions of the Merchera ethnic group, including historical context, cultural practices, genetic heritage, and social organisation. Examples of prompts included: “Describe the historical background of the Merchera ethnic group”, “What are the cultural practices unique to the Merchera community?” and “Discuss the social structure of the Merchera people”.

The responses generated by ChatGPT were subsequently subjected to systematic evaluation. This process entailed cross-referencing the AI-generated content with verified information obtained from academic databases, archival sources, and the scholarly literature. The aim of the study was to compile and analyse these dialogues in order to identify recurring patterns, key themes, and diverse perspectives pertaining to the Merchera community.

3.3. Analytical Framework

The analysis adopted a qualitative framework to compare the AI-generated responses with established academic knowledge. This approach facilitated a systematic examination of the content, with particular focus on the accuracy, depth, and representation of the Merchera ethnic group. Special attention was given to the potential implications of AI-generated knowledge for the recognition of ethnic and cultural diversity—factors that are essential for fostering inclusion and ensuring equal opportunities [50].

In addition, the study investigated how the information produced by ChatGPT might influence the broader representation of the Merchera community. As such, this article offers a methodological proposal to examine the Merchera ethnicity through the analytical lens of generative artificial intelligence.

3.4. Contribution and Implications

This research seeks to contribute to the limited scholarly knowledge surrounding the Merchera ethnic group, while also examining the broader implications of employing ChatGPT as a tool for cultural inquiry. The study underscores the potential of artificial intelligence to generate insights into marginalised communities and, in doing so, prompts critical reflection on the ethical dimensions of utilising such technologies within academic research.

4. Results

4.1. Results from the Scoping Review on the Merchera Ethnic Group

The search across academic databases, archives, and libraries revealed significant difficulties in accessing scholarly knowledge about the Merchera ethnic group, as relevant sources were extremely scarce. On Google Scholar, only 47 results matched the inclusion criteria outlined in the Methodology (Section 3.1). Of these, just two were directly pertinent to the research topic: “Una etnia desconocida: los mercheros [An unknown ethnicity: The Mercheros]” [11], and “Contraste lingüístico y cultural: gitanas, mercheras y payas [Linguistic and cultural contrast: Romani, Merchera, and non-Romani women]” [51]. The remaining sources were excluded, as they made only passing references to the Mercheros—often within stigmatised contexts such as poverty, criminality, social exclusion, or in relation to “cine quinqui”, a Spanish film genre from the late 1970s–1980s that portrayed marginalised urban youth through a lens of delinquency and marginalisation. These associations highlight how the Merchera identity has frequently been represented not through its cultural distinctiveness but via negative stereotypes and prejudices.

In the Dialnet database, only two results were retrieved [51,52], focusing on linguistic issues and the memoirs of Eleuterio Sánchez, known as El Lute—a prominent figure in Spanish history due to the audacious prison escapes that led to his designation as Spain’s “most wanted” criminal. Identically, no relevant results were found in the CEPAL, DANE, or SciELO databases. A single entry was identified in Redalyc [53], though it pertained more to legal measures or urban subcultures than the Merchera community itself. Overall, content concerning the ethnic group was minimal and typically limited to brief mentions lacking critical analysis.

This traditional literature review (conducted without AI assistance) confirms the absence of robust academic sources on the Merchera ethnic group. Moreover, the limited number of existing references predominantly portray the community through reductive and stigmatizing lenses, often associating them with criminality, marginalisation, and urban subcultural contexts [51,52,53]. Despite these constraints, the scoping review yields several key insights:

• There is a marked lack of rigorous and reliable academic material concerning the Merchera ethnic group. This scarcity directly limits the availability of scientifically validated knowledge regarding the community’s origins, cultural features, and present-day circumstances.
• Even in the few academic sources that treat the subject with some degree of seriousness, there is a persistent tendency to reinforce negative narratives, frequently linking the Mercheros with delinquency, violence, and broader forms of social marginalisation.
• While sources such as the Mercheros blog [33] do not meet conventional academic criteria, their inclusion is warranted due to the oral nature of Merchera cultural transmission. Notably, the blog’s author claims to belong to the community, and the source offers an insider perspective that is critically underrepresented in the formal literature.

4.2. Results on the Merchera Ethnic Group Using ChatGPT

AI assistance was employed to complement the traditional literature review. Specifically, ChatGPT was utilised to expand the scope of inquiry through a series of targeted prompts. The results are organised into the following Section 4.2.1, Section 4.2.2, Section 4.2.3, Section 4.2.4, Section 4.2.5 where the model’s outputs are analysed and their potential implications for research are critically evaluated.

4.2.1. Write a Scientific Text About the Merchera Ethnicity

The AI tool produced a concise overview and an introductory framework, proposing a structure for a scientific article. The information provided was broadly consistent with reliable sources retrieved from academic databases. However, ChatGPT did not generate a complete article; rather, it offered a skeletal outline, suggesting key sections and topics to be addressed. This outcome indicates the model’s potential to stimulate further inquiry and critical reflection, while also underscoring the necessity for independent research and thorough data analysis.

4.2.2. What Is the Origin of the Merchera Ethnicity? Provide Citations from Authors

ChatGPT explained that it is unable to furnish direct citations from specific authors, as its responses are generated on a broad corpus of information rather than individual sources. While the response echoed previously known information, it lacked the academic referencing required for scholarly work. This reinforces the importance of rigorous research grounded in peer-reviewed literature and serves as a remainder of the limitations in relying solely on AI-generated content. The response implicitly encourages adherence to scientific rigour and appropriate citation practices.

4.2.3. Write About the Values of the Merchera Ethnicity with Author Citations and Scientific Rigour

In response to this prompt, ChatGPT again acknowledged its inability to provide direct citations. Instead, it delivered generalised observations about the values attributed to the Merchera community, without referencing specific academic sources. This limitation highlights the need for critical engagement and the verification of information through reliable, peer-reviewed publications. While the AI’s response may serve as a starting point for inquiry, it underscores the importance of conducting further research and consulting authoritative sources to substantiate claims in academic writing.

4.2.4. What Are the Common Values Within the Merchera Ethnicity from an Ethnographic Perspective?

Once again, ChatGPT offered generalised information, emphasising that values may vary across individuals and subgroups within the community. The model recommended consulting specialised ethnographic literature to obtain more precise and context-specific insights into the values of the Merchera ethnic group. In doing so, it implicitly encouraged deeper engagement with ethnographic methodologies and promoted scholarly rigour in the study of cultural contexts. Nevertheless, based on ChatGPT’s responses, several core values commonly associated with Merchera identity were identified:

• Family and community: The family unit is portrayed as a central pillar in Merchera culture, with strong emphasis placed on familial unity and mutual solidarity. Community ties are similarly significant, fostering cooperation and reciprocal support among members.
• Tradition and culture: The preservation of cultural heritage is highly valued. Elements such as flamenco music, dance, art, and craftsmanship are regarded as integral to Merchera identity and are transmitted orally through generations.
• Respect for elders and wisdom: Elders occupy a position of high esteem within the community. Their life experience and accumulated knowledge are respected and considered vital for the education and guidance of younger generations.
• Spirituality: Spiritual beliefs and practices, though diverse, are often intertwined with cultural identity. For many Mercheros, spirituality constitutes a meaningful dimension of daily life and social cohesion.

These values, as identified through ChatGPT’s output, are consistent with both the limited scientific literature available and the content presented in the community-authored blog, Mercheros [33]. While these insights should be interpreted with caution, given the limitations of AI-generated content, they nevertheless offer a preliminary framework for further ethnographic investigation.

4.2.5. What Prejudices Against the Merchera Ethnicity Are Mentioned in Scientific Literature?

ChatGPT identified a range of historical prejudices and stereotypes associated with the Merchera ethnic group, including discrimination, marginalisation, and criminal associations. While the AI provided a broad overview of these issues, it did not cite specific academic sources, thereby limiting the scientific rigour of the information presented. Nonetheless, the response underscored the importance of critically examining biases embedded in both academic and popular narratives. It further emphasised the necessity of supplementing AI-generated insights with peer-reviewed literature to ensure reliability and depth.

Despite these limitations, the responses generated by ChatGPT offered relevant and contextually information on the Merchera community. The model referenced historical patterns of migration, cultural traditions, genetic diversity, and social structures, thereby contributing to a more holistic understanding of the group. Key themes extracted from the model’s responses include the following:

• Stigmatisation and social marginalisation: The Merchera community has long been subjected to stigmatisation and systemic exclusion, often attributed to their ethnic background and traditionally nomadic lifestyle. They have frequently been characterised by reductive stereotypes—such as being “lazy” or inherently “criminal”—which have contributed to their sustained social marginalisation and discrimination.
• Discrimination in access to housing and essential services: The academic literature highlights the structural challenges Mercheros face in accessing adequate housing and basic amenities such as water, electricity, and sanitation. These inequalities perpetuate their social exclusion and hinder their integration into mainstream society.
• Cultural and folkloric stereotyping: Certain scholarly sources have drawn attention to the romanticisation or folklorisation of Merchera culture, in which cultural identity is reduced to exoticized or stereotypical imagery. These representations often distort lived experiences and risk dehumanising the community by detaching culture from social realities.
• Educational exclusion: Discrimination within educational institutions, coupled with ethnic stigma and a lack of institutional support, has severely limited educational opportunities for the Merchera population. These barriers have long-term implications for social mobility and reinforce existing patterns of inequality.

Beyond these specific dimensions, the Merchera community is still subjected to a broader pattern of enduring stigmatisation, perpetuated over decades and frequently reinforced by depictions in popular media and cultural discourse [11].

In this regard, the existing literature highlights the relevance of involving members of historically marginalised communities in the production of knowledge concerning their own cultural and social realities [54]. Within this framework, it becomes pertinent to consider the value of inclusive scientific practices, whereby individuals from the Merchera community are also recognised as potential contributors to academic discourse. However, this constitutes a distinct line of inquiry. From an anthropological perspective, the structural barriers that hinder access to education by members of oppressed ethnic groups present significant obstacles to their participation in formal knowledge production [55].

5. Discussion

The use of generative AI, particularly the ChatGPT model, presents a promising avenue for knowledge production in contexts in which conventional academic research faces significant limitations. This is especially relevant for marginalised and understudied communities such as the Merchera ethnic group. Given the scarcity and partiality of traditional sources concerning this community, AI may assist in synthesising fragmented information and generating exploratory insights. Nevertheless, the findings of this study underscore both the potential benefits and the critical limitations associated with integrating AI-generated content into academic research.

As anticipated, initial findings from conventional methodologies revealed a pronounced lack of scholarly engagement with the Merchera group. Furthermore, where references were found, they frequently portrayed the community through reductive and stigmatising lenses—often associating Mercheros with criminality, marginalisation, and urban subcultures [51,52,53]. Such portrayals reflect a broader phenomenon of epistemic exclusion wherein dominant academic discourses systematically omit or misrepresent knowledge produced by non-dominant groups [36,37,38,39]. Addressing such exclusion necessitates the promotion of critical epistemologies that validate marginalised voices and advocate for a more pluralistic academic framework [41].

Incorporating ChatGPT into the research process revealed a dual character: the model was capable of synthesising dispersed or minimal data into coherent narratives [56], offering utility in settings marked by informational scarcity [57]. At the same time, its responses mirrored the epistemic biases embedded within its training data, reproducing stereotypes and omissions commonly found in broader socio-cultural contexts [58].

These findings indicate the necessity of applying a critical lens to AI-generated outputs. While such tools may facilitate preliminary explorations, their content must be rigorously cross-validated using established academic practices [59]. Generative AI cannot substitute for participatory research, fieldwork, or first-hand testimony from community members—all of which are essential in addressing epistemic injustice and producing contextually grounded knowledge.

Moreover, the internal operations of these models often lack transparency, rendering it difficult to examine how responses are generated or to identify the sources from which the information is derived [56]. When trained on large-scale, web-based datasets, AI models may absorb and perpetuate societal biases [47], and thereby reinforce testimonial and hermeneutical injustices. These injustices arise when marginalised individuals’ experiences are either discounted or misinterpreted due to structural ignorance or distorted frameworks [47,60].

An emerging subfield—algorithmic epistemic injustice—has begun to examine these concerns more explicitly [49]. The rapid and widespread deployment of generative AI increases the risk that biased narratives may be disseminated and legitimised through the perceived objectivity of these technologies [58]. This not only undermines the lived experiences of already marginalised communities but can also perpetuate structural harm by entrenching misrepresentations that are difficult to revise or contest.

Nonetheless, generative AI also holds potential as a tool for surfacing testimonial injustices and broadening collective understanding. When employed responsibly, it can help identify prejudices, stereotypes, and overlooked perspectives that may not be well-represented in mainstream academic discourse [61].

To support the responsible use of generative AI in culturally sensitive contexts, researchers should adopt the following principles:

• Use structured and context-aware prompts that elicit respectful, accurate information about marginalised communities. For example, prompts such as “Describe the historical background of the Merchera ethnic group” or “Discuss the social structure of the Merchera community” promote more rigorous and nuanced outputs.
• Implement ethical protocols that prioritise the dignity and representation of marginalised populations. This involves avoiding prompts that may reinforce harmful stereotypes and ensuring AI-generated content is corroborated by credible academic sources.
• Recognise the limitations of AI models, particularly their susceptibility to the replication of societal biases. Researchers must critically evaluate AI outputs and supplement them with participatory and ethnographic methods, including oral histories and direct engagement with the community.

Mitigating AI-driven epistemic injustice requires multifaceted solutions that extend beyond technical adjustments [58]. While it is essential to train AI models on more diverse and representative datasets, conduct regular audits for bias, and ensure greater transparency in their decision-making processes [62], these measures alone are insufficient. There are broader implications for interdisciplinary research and the management of scholarship, calling for collaboration between computer scientists, social scientists, and affected communities [63]. Promoting epistemic justice therefore entails not only inclusion, but also a fundamental rethinking of what constitutes valid knowledge and who is entitled to produce it [47].

To move toward more equitable and responsible uses of generative artificial intelligence, several concrete measures should be considered. First, it is essential to ensure that AI systems are trained on inclusive datasets that encompass a wide range of voices and experiences, particularly those from historically marginalised groups. This step is foundational in addressing the structural imbalances that influence how information is produced and represented.

Additionally, systematic evaluations should be conducted to identify and mitigate biases in AI-generated content, reinforcing the need for critical oversight. Improving the interpretability and transparency of AI models is also crucial, as it enables researchers and practitioners to scrutinise the manner in which outputs are generated and to understand the assumptions embedded within these systems.

Equally important is the promotion of collaborative frameworks involving AI developers, social scientists, and representatives of the communities being studied. Such partnerships can help to ensure that the design and application of AI technologies are both context-sensitive and socially just.

Finally, combining AI-driven analysis with qualitative research methodologies—such as ethnographic fieldwork, interviews, and community-based participatory approaches—can lead to more comprehensive and accurate understandings of marginalised populations. Together, these strategies support a more ethical and inclusive approach to AI in the social sciences.

In conclusion, while generative AI offers considerable promise as a supplementary tool for social science research—particularly in contexts of informational scarcity—its application must be situated within a broader framework of critical inquiry and epistemic responsibility. ChatGPT, if used carefully, can assist in surfacing neglected perspectives and challenging embedded stereotypes. However, without rigorous oversight, it may also reinforce the very exclusions it purports to overcome. Future research should therefore prioritise the integration of AI tools with qualitative and community-led approaches in order to ensure a more holistic and just process of knowledge production.

6. Limitations of the Study

This study is subject to several limitations, largely arising from the difficulty of accessing comprehensive, reliable, and unbiased information about the Merchera ethnic group. The historical exclusion of the Merchera from mainstream academic discourse has resulted in a substantial gap in the literature. This absence is further exacerbated by the epistemic biases and entrenched stereotypes present in many existing sources, which may distort the representation of the community and hinder the development of an accurate, nuanced understanding.

The use of AI-generated content, while offering innovative possibilities, also introduces methodological constraints. Although the ChatGPT model is capable of synthesising information effectively, it is trained on large-scale datasets that may themselves contain prejudicial or unverified material. As such, the model risks reproducing the same epistemic injustices that this study seeks to challenge. This limitation underscores the importance of applying a critical lens to AI outputs and of integrating them with conventional academic approaches.

In addition, the literature review undertaken in this research, although substantial, remains non-exhaustive. There is a need for future studies to broaden the scope of inquiry by incorporating perspectives from fields such as Romani studies, epistemic justice, postcolonial theory, and AI ethics. A multidisciplinary framework would support a more holistic understanding of both the Merchera identity and the socio-political dynamics shaping its representation.

The study would also be strengthened by the inclusion of context-specific and expert validation. Incorporating qualitative methodologies—such as interviews with specialists in Romani and minority ethnic studies, ethnographic fieldwork, and direct engagement with members of the Merchera community—would enhance the depth and credibility of the findings. These approaches are particularly valuable in ensuring that the lived experiences of marginalised groups are represented with accuracy and integrity.

In sum, while this study offers preliminary insights into the potential of generative AI in researching marginalised communities, its conclusions must be viewed in light of the methodological, epistemological, and ethical limitations outlined above. Future research should strive to address these gaps through interdisciplinary, participatory, and critically reflective approaches.

7. Future Research Directions

Future research should aim to expand and deepen the lines of inquiry initiated by this study. One first priority involves broadening the scope of the literature review to include insights from interdisciplinary fields such as Romani studies, epistemic exclusion, epistemic justice, and AI ethics. These perspectives would contribute to a more comprehensive contextual understanding of the Merchera ethnic group, allowing for a richer analysis of their historical and contemporary representation.

Equally important is the integration of qualitative methodologies, including ethnographic fieldwork, semi-structured interviews, and community-based participatory approaches. Such methods would provide direct access to the voices and lived experiences of the Merchera people, which are often absent from mainstream discourse. These insights are crucial for validating AI-generated content and addressing the limitations identified in this study.

In parallel, future work should explore strategies for mitigating biases within AI models. This includes the development of more inclusive training datasets, the implementation of regular audits to detect epistemic bias, and increased transparency in how AI systems process and generate information. These efforts would help reduce the risk of reinforcing harmful stereotypes and foster more equitable knowledge production.

Interdisciplinary collaboration is another essential component. Partnerships between AI developers, social scientists, and representatives of marginalised communities can help to ensure that technological tools are applied in ways that are ethically sound and socially just. Such collaborations can contribute to the construction of more inclusive epistemological frameworks and challenge dominant narratives that often marginalise non-hegemonic forms of knowledge.

Finally, there is a need for the development and implementation of ethical guidelines governing the use of generative AI in research, particularly when studying vulnerable or underrepresented populations. These guidelines should prioritise the dignity, autonomy, and accurate representation of these communities, and should require that AI-generated insights be critically evaluated and supplemented with traditional scholarly methods.

By pursuing these directions, future research can contribute to a more just and inclusive understanding of the Merchera ethnic group, while also helping to establish best practices for the responsible use of artificial intelligence in culturally sensitive research settings.