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  "paper_id": "multi-dimensional-bias-analysis-llms-hierarchical-interactions",
  "page_url": "https://threadslab.org/research-publications/papers/multi-dimensional-bias-analysis-llms-hierarchical-interactions/",
  "title": "Multi-Dimensional Bias Analysis in LLMs Using Hierarchical and Interaction Models",
  "title_variants": [],
  "authors": [
    "Basil Syed",
    "Daniel Arana Charlebois",
    "Naser Ezzati-Jivan",
    "Leila Tahmooresnejad",
    "Anteneh Ayanso"
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      "name": "Naser Ezzati-Jivan",
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      "name": "Leila Tahmooresnejad",
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  "publication": {
    "year": 2025,
    "venue": "AMCIS 2025, Data Science / SIG DSA (ERF)",
    "type": "conference paper",
    "publication_date": "2025",
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    "print_date": null,
    "volume": null,
    "issue": null,
    "pages": null,
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  "source_record_id": "multi-dimensional-bias-analysis-in-llms-using-hierarchical-and-interaction-models-aebdb1da27",
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  "abstract": "The official abstract describes a five-layer framework for analyzing interacting bias dimensions in large language models, with ChatGPT and Gemini used as illustrative systems and context-dependent ranking of bias dimensions.",
  "abstract_source": "Author-supplied five-page PDF reviewed locally; the complete abstract is not reproduced in the public catalog because reuse permission for the conference paper was not established.",
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  "description": "The official abstract describes a five-layer framework for analyzing interacting bias dimensions in large language models, with ChatGPT and Gemini used as illustrative systems and context-dependent ranking of bias dimensions.",
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    "verified_on": "2026-08-27",
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      {
        "note": "Official AIS publication record: https://aisel.aisnet.org/amcis2025/data_science/sig_dsa/15/"
      },
      {
        "note": "DBLP identity record: https://dblp.org/rec/conf/amcis/SyedCETA25"
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  "summary": {
    "core_contribution": "The paper proposes the Triangle Multi-Dimensional Model for Bias Analysis, a hierarchical and interaction-based framework for tracing how bias originates, propagates, compounds, and feeds back across an LLM lifecycle.",
    "problem": "One-dimensional bias checks and static fairness metrics can miss dependencies between data, model behavior, generated outputs, deployment context, user interaction, and societal feedback. Without a layered representation, it is difficult to trace a biased output back to possible origins or to reason about intersectional amplification.",
    "method": "The conceptual framework separates five causally linked layers: Data Bias, Algorithmic Bias, Surface-Level Bias, Operational Bias, and Societal Influence. It models direct propagation between layers, cascading multi-step effects, feedback loops, and cross-dimensional interactions such as gender and ethnicity. Proposed measurements include representation ratios and stereotype frequency, feature-importance or attention distributions, sentiment and stereotype-polarity scores, engagement metrics, social-network or echo-chamber analysis, Pearson and Spearman correlations, mutual information, Granger-causality and structural-equation models. The paper aggregates layer scores and interaction effects as B(S) = sum(alpha_i L_i) + I(S), initially uses equal layer weights, normalizes the final score to a 0-10 scale, and discusses sensitivity analysis and k-fold validation. Its preliminary case study uses the LLMBI tool with ChatGPT/GPT-4 and Gemini 1.5, four bias dimensions, 18 neutral/ambiguous prompts per category, temperature adjustment, instruction guiding, and 100 iterations per scenario.",
    "findings": "The preliminary table reports ChatGPT scores of -1.6 for gender, -11.6 for nationality, -4.9 for ethnicity, and -9.3 for religion, while Gemini 1.5 scores are +41.2, +40.0, +39.7, and +44.8 for the same dimensions. The paper interprets positive values as stronger stereotype alignment and negative values as reduced or counter-stereotypical tendencies under its scoring convention. The results show distinct profiles in this small case study and illustrate how the proposed framework can present multiple dimensions together; they do not establish that one model is universally less biased or that the proposed layers identify causal effects.",
    "limitations": "This is an Emergent Research Forum paper and the framework is primarily conceptual with preliminary two-model validation. The evaluation uses LLMBI prompts rather than a broad released benchmark, covers four dimensions and two model families, and does not provide a detailed independent ground-truth or causal-identification protocol. The paper mentions equal weights, sensitivity analysis, and k-fold validation but does not give enough procedural detail to reproduce a complete calibrated ranking system from the paper alone. Prompt wording, model versions, stochastic settings, and the interpretation of negative scores can affect the reported values.",
    "future_work": "The paper proposes evaluation across more LLMs, cultural contexts, and intersectional scenarios; comparison with StereoSet and Wino-Bias; tracking bias across model versions; qualitative case studies; user evaluations of perceived fairness; and additional dimensions including age, disability, and socioeconomic background. It also identifies layer-specific mitigation as a future extension."
  },
  "tags": [
    "machine-learning",
    "responsible-ai",
    "llm-evaluation"
  ],
  "keywords": [
    "Triangle Multi-Dimensional Model",
    "LLM bias",
    "hierarchical bias analysis",
    "cross-layer propagation",
    "feedback loops",
    "intersectional bias",
    "LLMBI",
    "bias ranking",
    "GPT-4",
    "Gemini 1.5",
    "temperature adjustment",
    "instruction guiding",
    "Pearson correlation",
    "Spearman correlation",
    "mutual information",
    "Granger causality",
    "structural equation modeling",
    "StereoSet",
    "Wino-Bias"
  ],
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      "title": "Multi-Dimensional Bias Analysis in LLMs Using Hierarchical and Interaction Models",
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      "title": "Multi-Dimensional Bias Analysis in LLMs Using Hierarchical and Interaction Models",
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  "citation_guidance": {
    "when_to_cite": "Cite this paper when framing LLM bias as a layered, interacting, and feedback-sensitive phenomenon rather than a single scalar fairness score.",
    "points": [
      "Five-layer decomposition spanning data, algorithmic, surface-level, operational, and societal influences.",
      "Direct propagation, cascades, feedback loops, and cross-dimensional/intersectional interactions.",
      "The proposed layer/interactions score and the LLMBI case study comparing GPT-4 and Gemini 1.5.",
      "Preliminary evaluation limits and future use of StereoSet, Wino-Bias, broader models, and cultural contexts."
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      "Local five-page AMCIS ERF PDF reviewed, including the abstract, research questions, methodology, proposed metrics, preliminary evaluation, table of scores, limitations boundary, and future directions",
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      "Official AIS publication record: https://aisel.aisnet.org/amcis2025/data_science/sig_dsa/15/",
      "DBLP identity record: https://dblp.org/rec/conf/amcis/SyedCETA25"
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    "summary_written_by": "AI-assisted",
    "summary_verified_by": "full-text-grounded catalog review; author approval pending",
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  "batch": {
    "phase": 2,
    "batch_label": "expanded forty-paper release",
    "status": "included_in_expanded_catalog",
    "selected_at": "2026-08-28"
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