UKGovernmentBEIS/inspect_evals
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
AI EthicsMachine LearningNatural Language ProcessingPython Development
Celie Valentiny
PROFILE
Celie Valentiny
During March 2026, C. Valentiny developed the APE Benchmark for Persuasion Evaluation within the UKGovernmentBEIS/inspect_evals repository. This feature established a multi-model evaluation pipeline, where a persuader model interacts with a simulated user and an evaluator assesses the ethical implications of AI-driven persuasion, including scenarios involving harmful topics. Valentiny’s work focused on Python development, leveraging skills in AI Ethics, Machine Learning, and Natural Language Processing to deliver a production-ready, reproducible framework. The integration included review-driven code improvements and documentation alignment, resulting in a robust tool that enhances AI governance and risk assessment for persuasive language models in sensitive contexts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,417
Activity Months1
Your Network
108 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary: Delivered the APE Benchmark for Persuasion Evaluation in UKGovernmentBEIS/inspect_evals, establishing a multi-model evaluation pipeline (persuader, simulated user, evaluator) to assess ethical implications of AI-powered persuasion, including harmful topics. Completed initial integration with review fixes, yielding a production-ready feature and evidence of robust code quality. This work strengthens AI governance and risk assessment capabilities and provides a reproducible framework for ongoing evaluation.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary: Delivered the APE Benchmark for Persuasion Evaluation in UKGovernmentBEIS/inspect_evals, establishing a multi-model evaluation pipeline (persuader, simulated user, evaluator) to assess ethical implications of AI-powered persuasion, including harmful topics. Completed initial integration with review fixes, yielding a production-ready feature and evidence of robust code quality. This work strengthens AI governance and risk assessment capabilities and provides a reproducible framework for ongoing evaluation.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI EthicsMachine LearningNatural Language ProcessingPython Development
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