mlsanityData to 14 Oct 2026

Whose Alignment? Comparing LLM Process Alignment Across Diverse Organizational Decision Contexts

arXiv 2605.25256 ↗First posted 24 May 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Steerable pluralism requires a model to faithfully represent one specified perspective. Organizations are a natural setting for this demand, since they deploy LLMs to make decisions that must reflect their own policy. Yet, most existing work fixes that perspective at the level of individuals or demographic groups. We rely on a decision-policy capturing method to measure process alignment in organizational settings, assessing whether an LLM faithfully reproduces the organization's decision policy rather than merely reaching the same conclusions. We find heterogeneity along two axes. Across models, baseline alignment varies strongly and tracks neither pricing nor general benchmark performance. Across organizations, the structure of alignment changes. In ECHR Article 6 decisions, process alignment predicts output accuracy (r = 0.85, p < .001), and making the organization's past decision policy explicit improves poorly aligned models. In consumer credit decisions, process alignment is low overall but varies more than output accuracy, and the models resist adopting the organization's weighting of protected attributes. Because historical credit decisions encode potentially discriminatory patterns, higher alignment there is not always desirable. Process-level measurement is therefore necessary, and depending on whether the target policy is normatively desirable, the same procedure can calibrate or audit a model. Deciding which policy to align to, and whether higher alignment is feasible or desirable, makes organizational alignment a pluralistic problem in its own right.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 24 May 2026.

Builds on

9 of 26 references.

References · 9

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2020Shortcut Learning in Deep Neural Networkscited by 1,969
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  4. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  5. 2020Artificial Intelligence, Values and Alignmentcited by 448
  6. 2022Using cognitive psychology to understand GPT-3cited by 398
  7. 2024A Roadmap to Pluralistic Alignmentcited by 184
  8. 2021Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Casescited by 77
  9. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Value Alignment Tax: Measuring Value Trade-offs in LLM Alignment
  2. 2026AI Alignment Breaks at the Edge
  3. 2024Chat Bankman-Fried: an Exploration of LLM Alignment in Finance
  4. 2025Societal Alignment Frameworks Can Improve LLM Alignment
  5. 2025Beyond Ordinal Preferences: Why Alignment Needs Cardinal Human Feedback
  6. 2025Evaluating Intra-firm LLM Alignment Strategies in Business Contexts
  7. 2026COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
  8. 2025Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation