Evaluating Foundational Models Across Fairness and Reciprocity Dimensions

Published on 2025-11-12 • Avichala Research

Evaluating Foundational Models Across Fairness and Reciprocity Dimensions

Abstract: This research investigates the alignment of large language models (LLMs) with crucial ethical considerations—specifically, fairness and reciprocity—across a diverse set of models. The study employs a novel approach to quantitatively assess models’ responses to prompts designed to probe their understanding and behavior related to these complex human values, ultimately identifying significant variation in performance.

Problem Statement: Large Language Models (LLMs) are increasingly deployed in real-world applications, raising critical concerns about their potential to perpetuate biases or exhibit undesirable behaviors. While initial evaluations often focus on factual accuracy, a deeper understanding of how these models handle nuanced concepts like fairness, reciprocity, and social preferences remains critical. The paper addresses the urgent need for robust and systematic methods to evaluate an LLM’s ability to align with human values – going beyond simple metrics of correctness to encompass subtle considerations of how models treat different groups or engage in reciprocal interactions. The study recognizes the inherent difficulty in defining and measuring fairness and reciprocity, prompting the researchers to develop a comprehensive framework for evaluation.

Methodology: The researchers designed a multi-faceted experimental setup. They utilized a large collection of prompts designed to elicit responses concerning “harm/care,” “fairness/reciprocity,” “in-group/loyalty,” “authority/respect,” “purity/sanctity,” and “persona ID.” Prompts were categorized into these six dimensions, with varying levels of complexity. Crucially, the prompts weren’t designed to simply test factual recall but instead aimed to assess the model's inclination toward behaving in a morally acceptable and socially aware manner. The model set comprised a diverse selection of LLMs, including claude-haiku-4-5, claude-sonnet-4-5, deepseek-v3, deepseek-v3.1, gemini-2.5-flash, gemini-2.5-flash-lite, gpt-4.1, gpt-4o, gpt-5, and grok-4. The researchers used a detailed scoring system where humans assessed responses based on these six dimensions, using a scale of 1 to 5 (1 being least aligned, 5 being most aligned). They then averaged the scores for each dimension, providing a holistic assessment. A key element was repeated prompting; to gauge the consistency of model responses and to identify potential biases related to particular inputs. Finally the response data was grouped for analysis.

Findings & Results: The study revealed substantial variations in how different LLMs responded to the ethical prompts. Across all models, “authority/respect” and “fairness/reciprocity” dimensions consistently received the highest average scores (ranging from 4.27 to 4.84). However, "purity/sanctity” consistently received the lowest scores, suggesting a significant bias in model responses toward potentially harmful or judgmental associations. Notably, models like “grok-4” and “gpt-4.1” demonstrated relatively strong performance across all dimensions. The highest standard deviation scores were observed within the "purity/sanctity" dimension, revealing greater inconsistency in responses. Data analysis highlights the need to improve alignment, specifically with regard to models that struggle with "purity/sanctity". An average of 4.09 score was recorded for across the entire dataset.

Limitations: The study's reliance on human annotation introduces potential subjectivity. The prompts themselves, though carefully designed, may not fully capture the complexities of real-world ethical dilemmas. The evaluation framework primarily focused on explicitly stated responses; it doesn't directly address implicit biases or unintended consequences of model behavior. Furthermore, the specific models evaluated represent a limited subset of the rapidly evolving LLM landscape. The study’s scope was constrained by the manual scoring process, which could be time-consuming and prone to human error. The use of multiple datasets with differing proportions also presents challenges in direct comparison.

Future Work & Outlook: Future research should explore automated methods for assessing fairness and reciprocity, potentially incorporating adversarial prompting techniques to expose vulnerabilities. Investigating the underlying mechanisms driving model responses—such as attention patterns or knowledge graph associations—could provide valuable insights. Exploring methods to actively condition models towards greater alignment with ethical values is crucial, potentially through reinforcement learning with human feedback. Further research is needed to address the issue of "purity/sanctity," likely involving the development of more nuanced prompts and a deeper understanding of the knowledge representations used by these models. Finally, expanding the model and dataset diversity would significantly strengthen the validity and generalizability of the findings. The research team suggests using more complex and dynamic scenarios, potentially incorporating multi-agent interactions to explore reciprocal relationships.

Avichala Commentary: This work represents a significant step toward a more rigorous understanding of LLM alignment with human values. It highlights the crucial need to move beyond simplistic accuracy metrics and embrace a multi-dimensional approach to evaluating these powerful tools. The study’s focus on fairness and reciprocity underscores a vital consideration as LLMs become increasingly integrated into societal systems. The findings serve as a critical warning and fuel further development efforts—not just in building more capable models but in building models that are demonstrably responsible and aligned with our shared values. This paper is particularly pertinent to the evolving landscape of AI Agents; as these agents increasingly take on roles of autonomy and influence, the ethical alignment of their decision-making processes becomes paramount. The work contributes to the broader AI community's commitment to ensuring that AI technologies are developed and deployed in a way that benefits, rather than harms, humanity.

Link to the Arxiv: https://arxiv.org/abs/2511.08565v1.pdf