arXiv:2605.27168v1 Announce Type: new Abstract: Text embeddings are widely used to analyse large corpora of complex texts. However, it is unclear whether the embeddings capture the same semantic distances as the human experts using them. Ensuring alignment between embedding representations and human intentions is essential for valid analyses. We present the Stakeholder Grounding Exercise, a method for making expert associations explicit and grounding embedding model results in human understanding. In our primary case study on Danish policy issues, we find that neural text embeddings are substa
The proliferation of advanced AI systems and text embeddings necessitates robust methods to ensure their outputs are ethically aligned and interpretable by human experts, especially as AI integrates into critical decision-making processes.
Ensuring that AI models accurately reflect human understanding and intentions is crucial for the reliability, trustworthiness, and widespread adoption of AI in sensitive domains like policy and defense.
The proposed 'Stakeholder Grounding Exercise' offers a structured method for explicitly aligning AI text embeddings with expert human associations, moving AI development towards greater transparency and human-centric validation.
- · AI ethicists and researchers
- · Policy makers and analysts
- · National security agencies
- · Data scientists developing robust AI
- · Developers of ungrounded AI models
- · Organizations relying on black-box AI
- · Abstract AI research without practical application
Improved interpretability and trustworthiness of AI systems in specialized domains.
Accelerated adoption of AI in highly regulated or sensitive sectors due to increased confidence in model alignment.
Potential for new regulatory frameworks requiring explicit 'stakeholder grounding' or similar validation for AI deployed in public services.
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Read at arXiv cs.CL