Western-Dominance Bias in Global Systems

Updated 13 August 2025

Western-Dominance Bias is a phenomenon where Western cultural norms disproportionately influence media, academic discourse, and algorithmic design.
Empirical studies show that this bias skews media coverage and data representation, reinforcing hegemonic narratives and marginalizing non-Western viewpoints.
Mitigation strategies like dataset diversification and culture-aware tuning offer actionable pathways to rebalance global perspectives in technology and research.

Western-Dominance Bias is a pervasive phenomenon across information systems, computational models, and scientific disciplines where Western-centric cultures, norms, narratives, or value systems disproportionately shape data, model outputs, methodologies, and theoretical orientations. This bias manifests not only in overt content preferences but also within structural workflows, algorithmic designs, and evaluative criteria, ultimately privileging Western perspectives while marginalizing or misrepresenting non-Western or minority viewpoints.

1. Manifestations in Media and Information Systems

Western-dominance bias is empirically evident in both media coverage and digital content platforms. Studies of news and social media streams reveal strong Western-centric coverage, with Western traditional media and social media heavily favoring topics and celebrities born in the USA. For example, 88% of celebrity mentions in Western social media refer to USA-born figures, contrasting sharply with a more balanced 52%/48% split in Chinese social media mentions of Chinese vs. USA-born celebrities. Additionally, when cross-aligning topics between Chinese and Western traditional media, only 43% overlap, and a mere 4% of aligned topics focus on events in China, indicating attention is primarily directed toward Western or internationally salient events (Wurzer et al., 2017).

Domain-level audits of search engines further expose monopolistic structures: video search results for English queries are highly dominated by Western platforms, with YouTube occupying the top results for Bing, DuckDuckGo, and Yahoo, and Google's own search curating for diversity but still omitting competitor platforms such as Vimeo or Dailymotion. Importantly, source diversity is consistently lower in Russian query results, suggesting that algorithmic design and underlying repository structure reinforce Western platform centrality (Urman et al., 2021).

2. Ideological Homogeneity in Academic and Scientific Discourse

Bibliometric and network analyses identify Western-dominance bias at the author, institution, and country levels within academic publishing, notably in entrepreneurial finance research. The top contributing scholars and institutions are overwhelmingly located in the USA, UK, or Italy, with the USA's Y-index far exceeding that of non-Western countries (511, 0.795 vs. China's 10, 0.588). Co-authorship networks confirm collaboration clusters are largely confined to Western institutions with minimal cross-regional ties, reinforcing homogeneous core ideologies and limited tolerance for heterogeneity (Nguyen et al., 2020).

This pattern is mirrored in computational social computing fields. Analysis of ICWSM conference papers from 2018–2022 indicates that datasets are predominantly sourced from Western, affluent, and politically democratic regions. While ICWSM achieves lower solely Western dataset reliance compared to CHI or FAccT, there remains a substantial alignment with WEIRD variables: τ ≈ 0.36 (Educated), 0.49 (Rich), 0.32 (Democratic), suggesting the research outputs may not generalize to global, non-Western populations (Septiandri et al., 4 Jun 2024).

3. Western-Dominance in Machine Learning and Language Technologies

LLMs and vision-LLMs (VLMs) consistently exhibit Western-cultural bias at multiple operational levels:

Token and Word Association: Word association tests reveal a higher probability for Western (American, British) associations, with standard LLMs favoring USA > UK > Oceania > China schemas. Linear steering mechanisms (CultureSteer) targeting culturally-specific semantic representations significantly improve alignment metrics (PWR@20 jumped from 42.54 baseline to 80.13 post-intervention) over prompt-based methods (Dai et al., 24 May 2025).
Entity and Sentiment Recognition: Even in Arabic, multilingual and monolingual LMs assign greater likelihoods to Western-associated entities under Arab-contextualized prompts. NER and sentiment analysis tasks display clear performance gaps, with F1 scores for Western entities exceeding those for Arab counterparts by up to 20 points, and models systematically associating Arab entities with negative sentiment (Naous et al., 2023).
Visual and Multimodal Tasks: VLMs built on English-dominated corpora perform markedly better on Western visual splits (e.g., Dollar Street images) than on East Asian splits, which stems from pre-training language configuration. Models pre-trained on a balanced English/Chinese mixture display lower Western bias, regardless of prompt language, revealing deep representational roots to the pre-training corpus (Ananthram et al., 17 Jun 2024).
Creative Outputs: Writing assistants trained on Western-centric corpora homogenize writing. In controlled cross-cultural experiments, Indian users, exposed to Western-biased AI suggestions, shifted stylistically toward Western norms, with cross-cultural cosine similarity in essays rising from 0.48 (no AI) to 0.54 (with AI). Classification accuracy for author origin dropped by 7 percentage points post-AI intervention, illustrating the erasure of local literary style (Agarwal et al., 17 Sep 2024).

4. Socio-Cultural and Normative Impacts

Western-dominance bias directly affects public perception, explanatory needs, and societal value systems:

Explainable AI (XAI) Systems: Nearly all surveyed XAI studies (over 80%) are based on WEIRD samples and test only internalist explanations typical of Western, individualist cognitive styles, assuming that mental state-centered model explanations are universally preferred. Collectivist cultures, in contrast, exhibit a preference for externalist explanations centering on context, social roles, and norms, a dimension largely unaddressed in XAI design and evaluation (Peters et al., 28 Feb 2024).
Fairness Metric Preferences: International survey results reveal strong national-context effects, even among Western nations. For instance, France prefers quantitative parity metrics while the USA tends toward equal opportunity, and China exhibits scenario-specific metric selection. Personal attributes (gender, religion) impart only modest secondary effects, confirming national context dominates public metric preference. This indicates that the historical dominance of Western-derived fairness metrics may inadequately accommodate global perspectives (Sasaki et al., 24 Mar 2024).
Social Norm Reasoning in LLMs: Norm generation tasks from culturally distinct narratives reveal that default outputs from GPT-4, while accurate, are less culture-specific and more generic, with norms for China rated as significantly less specific than human-authored ones (difference ≈ +0.2 points, p = 0.001). Jensen-Shannon Divergence identifies GPT-4’s stances as closest to U.S. norms (JSD ≈ 0.12) and furthest from Chinese (JSD ≈ 0.33), highlighting entrenched alignment with Western cultural frameworks (Liu et al., 23 May 2025).

5. Bias Mitigation: Methodologies and Approaches

A range of mitigation strategies has been proposed and tested:

Bibliometric and Mindsponge Analysis: Mapping ideological nuclei and social structure using Y-index metrics and co-author networks provides a replicable framework to diagnose and track homogeneity in scholarly domains, with potential extension to other scientific fields (Nguyen et al., 2020).
Dataset Diversification: Empirical audits emphasize the role of training corpora in introducing bias. For instance, Arabic Wikipedia and globally-sourced web-crawled data were conclusively identified as Western-centric even in Arabic-language contexts. Local news and social media sources provide richer, less biased cultural representation (Naous et al., 2023).
Culture-Aware Steering Methods: Semantic representation steering mechanisms (CultureSteer), which apply linear transformations conditioned on target culture control vectors, outperform prompt-engineering in realigning LLM outputs and capturing latent cross-cultural semantic associations (Dai et al., 24 May 2025).
Game-Theoretic Negotiation: A novel Nash equilibrium-based negotiation process (Policy-Space Response Oracles, PSRO) enables regional cultural agents to iteratively explore possible guideline alignments, balancing acceptance with self-consistency. Perplexity-based acceptance and value self-consistency scores quantify convergence toward fair, mutually intelligible compromise while retaining core cultural principles (Zhang et al., 16 Jun 2025).

6. Practical and Global Implications

The implications of Western-dominance bias are multifaceted:

Reinforcement of Hegemonic Narratives: The amplification of Western narratives through media, language technologies, and information systems contributes to cultural hegemony and potential marginalization of alternative perspectives.
Algorithmic Monoculture: Model outputs—whether recommendations, search rankings, or generative suggestions—may inadvertently stifle local diversity, erode cultural expression, and perpetuate a global feedback loop reinforcing Western norms.
Policy and Evaluation Frameworks: A one-size-fits-all approach to fairness, harm detection, and disability standards exhibits poor alignment with local realities. Centering local expertise, building culturally-grounded datasets, and incorporating regional evaluators are recommended paths to more robust global standards (Phutane et al., 22 Jul 2025).
Recommendations for Inclusivity: Expanding research checklists, reporting the demographic origins of datasets, and encouraging diverse author collaborations are practical steps to foster transparency and methodological fairness (Septiandri et al., 4 Jun 2024).

7. Open Challenges and Future Directions

Structural Data Reform: Corrections to Western-centric pre-training datasets, adoption of more localized sources, and broader inclusion of minority viewpoints in annotation and evaluation pipelines remain key open problems.
Normative Design: Designing models that can flexibly generate explanations, recommendations, or outputs attuned to both internalist and externalist reasoning styles is an unresolved technical challenge.
Cross-Cultural Consensus Modeling: Extending negotiation frameworks to multi-party settings and complex value intersections has only begun to be explored.
Standardization and Regulation: The establishment of global standards for ableism, fairness, and explanation must move from export of Western definitions to international collaboration. Hybrid evaluation metrics—quantifying both numerical alignment and qualitative interpretative reasoning—will be necessary.

Western-Dominance Bias thus represents not merely a content-level imbalance, but a deeply ingrained structural, algorithmic, and epistemological phenomenon spanning multiple technological and scientific regimes. The detection, quantification, and remediation of this bias require systematic methodological innovation, institutional reflection, and persistent global collaboration.