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Cultural Flattening Score in AI

Updated 13 August 2025
  • Cultural Flattening Score is a metric that quantifies the loss of cultural diversity in AI outputs by comparing observed signals to expected culturally rich markers.
  • It employs divergence measures, variance, entropy, and mean absolute differences to assess how training data biases and model architectures favor dominant cultural standards.
  • Benchmark evaluations reveal that data imbalance, design protocols, and evaluation methods drive flattening, urging the adoption of more culturally diverse training and testing practices.

The Cultural Flattening Score (CFS) is an emergent conceptual and evaluative construct in recent AI and HCI research, designed to quantify the degree to which machine-generated outputs homogenize culturally distinctive patterns—averaging, compressing, or neutralizing diversity found in language, values, behaviors, artifacts, or interfaces. Across multiple domains (text, image, multimodal, VQA), the metric captures loss of nuance and convergence toward globally prevalent or dominant cultural standards, often Western-centric, due to training data bias, model architecture, or insensitive alignment protocols.

1. Conceptual Definition and Origins

Cultural flattening refers to the reduction, neutralization, or homogenization of culturally divergent signal in model output. This effect is documented in qualitative analyses ("softmaxing culture" (Mwesigwa, 28 Jun 2025)), empirical benchmarking for language and vision models (Schneider et al., 19 Feb 2025, Nayak et al., 15 Jul 2024), and theoretical frameworks for cultural measurement (Benedictis et al., 2020). The phenomenon is largely noted in systems trained on large-scale, web-mined datasets dominated by Western languages and cultural content, leading to outputs that favor the most statistically frequent or "head" distributions, marginalizing or misrepresenting long-tail (minority, local, or distinct) cultures.

The metaphor "softmaxing culture" (Mwesigwa, 28 Jun 2025) is instructive: as the softmax function compresses a vector to highlight high-frequency elements, so do AI models concentrate on dominant cultural markers, suppressing unique or low-frequency variants.

2. Quantitative Formulations

Research papers operationalize Cultural Flattening Score using various metrics tailored to their evaluation domains:

  • Score Based on Divergence from Expected Cultural Markers:

For interface design (Khanum et al., 2012), flattening is modeled as

CF=αMobsMexpected+βGCF = \alpha |M_\text{obs} - M_\text{expected}| + \beta G

where MobsM_\text{obs} is the observed magnitude/frequency of cultural features, MexpectedM_\text{expected} is the expected (e.g., Hofstede-derived) magnitude, GG is the extent of global design standard adoption, and α\alpha, β\beta are weights. A higher score indicates more flattening.

  • Variance and Entropy in Cultural Representation:

In benchmarking models for cultural knowledge (Schneider et al., 19 Feb 2025, Mushtaq et al., 14 May 2025), CFS is derived from standard deviation or normalized entropy of model output across cultural categories:

CFS=1σperformanceμperformanceCFS = 1 - \frac{\sigma_\text{performance}}{\mu_\text{performance}}

or, using entropy of perspective distributions (SS) for nn categories:

H=ipilog(pi),S=H/log(n)H = - \sum_i p_i \log(p_i),\quad S = H / \log(n)

where SS close to 1 signals well-balanced pluralism, SS near zero signals flattening.

  • Mean Absolute Difference to Human Baseline:

For moral or value questionnaires (Münker, 14 Jul 2025), flattening is measured via mean absolute difference mdmd between model and human responses:

md=1Ni=1Nrmodel(i)rhuman(i)md = \frac{1}{N} \sum_{i=1}^N |r_\text{model}^{(i)} - r_\text{human}^{(i)}|

Lower mdmd means better alignment; persistent low variance across cultures (even with high mdmd) evidences flattening.

  • Feature-Based and Aggregated Marker Comparisons:

In text-to-image evaluation (Kannen et al., 9 Jul 2024, Rege et al., 9 Jun 2025), scoring includes diversity measures such as Vendi score and marginal information attribution:

VSq(X;k)=exp(11qlogi(λi)q)VS_q(X; k) = \exp \left( \frac{1}{1-q} \log \sum_i (\lambda_i)^q \right)

Quality-weighted Vendi scores factor in both diversity and output quality, with low scores indicating cultural flattening.

3. Benchmarking Approaches

Several key cultural benchmarking suites operationalize CFS:

Benchmark Domain Scoring Principle
CDEval (Wang et al., 2023) LLMs Variance across Hofstede dimensions/domains
CUBE (Kannen et al., 9 Jul 2024) T2I Human annotation (awareness), diversity (Vendi)
CulturalVQA (Nayak et al., 15 Jul 2024) Vision QA Region/facetwise accuracy gaps
GIMMICK (Schneider et al., 19 Feb 2025) LVLMs Inter-region std. dev, relaxed accuracy, perplexity
CuRe (Rege et al., 9 Jun 2025) T2I Marginal information attribution/diversity
LLM-GLOBE (Karinshak et al., 9 Nov 2024) LLMs Open/narrative ratings, scale usage bias
WorldView-Bench (Mushtaq et al., 14 May 2025) LLMs PDS entropy (perspectives distribution score)

These frameworks consistently find that models flatten cultural variation, particularly for less-represented cultures and low-resource languages.

4. Drivers and Causes of Flattening

Several mechanisms have been identified as primary drivers:

  • Training Data Imbalance:

Overrepresentation of Western (English, European/North American) sources in corpora (Cao et al., 2023, Sukiennik et al., 11 Apr 2025, Kannen et al., 9 Jul 2024) leads to central tendency bias.

  • Model Architecture and Optimization:

Large-scale models, particularly those with strong regularization or temperature parameters biased toward modal outputs, tend toward flattened, average responses (Masoud et al., 2023, Mushtaq et al., 14 May 2025).

Monolingual or region-centric fine-tuning anchors models in the culture of the dominant language (Masoud et al., 2023).

  • Evaluation Protocols:

Check-list or closed-form evaluations can obscure nuanced cultural signals; free-text/narrative/crowdsourced approaches recover more local detail (Mwesigwa, 28 Jun 2025, Karinshak et al., 9 Nov 2024).

  • Multiplicity of Model Perspective:

Multiplexing via multi-agent systems or expert persona prompts increases representation balance and raises entropy scores (Mushtaq et al., 14 May 2025).

5. Impact and Implications

Cultural flattening has broad ramifications:

  • Interface Design:

As shown in analysis of Arabic interfaces (Khanum et al., 2012), global standards dilute local cultural identity.

  • Model Deployment:

Flattening undermines trust, user satisfaction, and correct representation in non-Western contexts (Wang et al., 2023, Nayak et al., 15 Jul 2024).

  • Social Science Validity:

Use of LLMs as "synthetic populations" is fundamentally challenged when variance is suppressed (Münker, 14 Jul 2025).

  • Bias and Equity:

Flattening perpetuates cultural stereotypes and exacerbates marginalization (Sukiennik et al., 11 Apr 2025, Kannen et al., 9 Jul 2024).

  • Mitigation Strategies:

Incorporating culturally diverse corpora, multilingual conditioning, fine-grained reward modeling, and multiplexed multi-agent prompt strategies markedly reduce flattening (Feng et al., 26 May 2025, Mushtaq et al., 14 May 2025).

6. Recent Proposals and Theoretical Critiques

Recent position papers (Mwesigwa, 28 Jun 2025) argue for a shift away from static, checklist-style cultural evaluation toward context-aware, relational, and narrative-centered methodologies. The metaphor "softmaxing culture" emphasizes the need to move from "What is culture?" to "When is culture?"—asking in which contexts, localities, or interactions cultural signals become meaningful.

As such, the Cultural Flattening Score itself is less a static metric and more a multi-dimensional diagnostic tool reflecting both statistical and qualitative variance in model outputs against a reference of expected cultural richness.

7. Future Directions

Research has articulated several pathways to improve cultural alignment and reduce flattening:

  • Expansion of Cultural Dimensions:

Beyond Hofstede and GLOBE frameworks, consideration of additional value systems, long-tail artifacts, and local practices is recommended (Wang et al., 2023, Karinshak et al., 9 Nov 2024).

  • Open-Ended Generation Benchmarks:

Automated and scalable assessment of narrative or generative outputs will better capture nuanced, context-dependent cultural intelligence (Mwesigwa, 28 Jun 2025, Karinshak et al., 9 Nov 2024).

  • Continuous Multilingual and Multiplex Training:

Adaptive learning incorporating language, regional cues, and perspectives sampling (Mushtaq et al., 14 May 2025, Feng et al., 26 May 2025).

  • Socio-Technical and Human-in-the-Loop Evaluation:

Integrated ML and HCI methodologies foreground relational aspects and contextual emergence of cultural signal (Mwesigwa, 28 Jun 2025).

  • Expanded Inclusion in Data and Development Stages:

Ground-up augmentation of training and evaluation datasets with long-tail, underrepresented cultural inputs (Schneider et al., 19 Feb 2025).

Summary Table: Flattening Metrics Across Key Benchmarks

Paper/Benchmark Flattening Metric Key CFS Indicator
(Khanum et al., 2012) (Arabic UI) Deviation from markers + norms High global norm adoption
(Benedictis et al., 2020) (Networks) JD network component Low network distance
(Cao et al., 2023) (ChatGPT) SD/correlation of dimension Lower SD, more flattening
(Wang et al., 2023) (CDEval) Variance across domains Low domain variance
(Kannen et al., 9 Jul 2024) (CUBE) Quality-weighted Vendi Low qVS: flattened images
(Schneider et al., 19 Feb 2025) (GIMMICK) Inter-region SD or CV Lower SD = more flattening
(Mushtaq et al., 14 May 2025) (WorldView) PDS entropy Low entropy = flattening
(Mwesigwa, 28 Jun 2025) (Softmax) Contextual diversity (concept) Homogenization by softmax
(Münker, 14 Jul 2025) (Morals) Mean absolute diff/ANOVA Low variation = flattening

The Cultural Flattening Score, therefore, is a meta-metric—spanning statistical, network, variance, recall/precision, and entropy-based measures—diagnosing the extent to which model outputs converge on a generic baseline and correspondingly lose the richness and distinctiveness expected in authentic cultural representation. It is central to improved model development, ethical deployment, and socio-technical evaluation of AI systems in global applications.

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