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Evidence record 648 · automatically gathered

Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback

Training safe Reinforcement Learning (RL) systems is inherently challenging, with no guarantee of avoiding unwanted behaviors. The most effective defenses against this are (i) transparency through explainability and (ii) alignment via human feedback. While both show promising results, no publicly available framework currently combines them. To address this, we introduce Themis, an XAI-enabled testing and evaluation framework for Reinforcement Learning from Human Feedback. Themis supports over 20

Record details

Published: 23 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (23 June 2026), “Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback,” evidence record 648, https://ethics.ai/record/648 (originally published by arXiv).

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