UKGovernmentBEIS/inspect_evals
Feb 2026 – Feb 2026
1 Month active
Languages Used
Python
Technical Skills
API developmentPythondata sciencemachine learningtesting
Declan McKenna
PROFILE
Declan Mckenna
During February 2026, Deco354 developed the MORU Benchmark for AI moral reasoning within the UKGovernmentBEIS/inspect_evals repository. They designed a modular, type-safe framework in Python, leveraging Pydantic models and factory patterns to enable reliable dataset loading, scoring, and evaluation. Deco354 introduced the Europa cad_bench components, including a 35-question dataset and custom question types, and implemented HuggingFace-backed dataset loading to ensure reproducibility and rate-limit resilience. Their work unified previous benchmarking assets under a single structure, expanded test coverage, and provided comprehensive documentation. This approach enabled repeatable, auditable AI ethics benchmarking, supporting policy-relevant decision-making in data science and machine learning contexts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,850
Activity Months1
Your Network
108 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 (Month: 2026-02): Delivered MORU Benchmark for AI moral reasoning within UKGovernmentBEIS/inspect_evals. Implemented a modular, type-safe MORU/CAD-Bench framework with dataset loading, scoring criteria, tests, and documentation. Introduced Europa cad_bench components, including EuropaQuestion types and a 35-question dataset, with a memory-dataset factory and HuggingFace-backed loading to improve reproducibility. Completed a major refactor to unify benchmark structure under MORU, including renaming CAPS Bench to MORU and consolidating Cad_Bench assets. Added end-to-end evaluation artifacts (graphs, plotting scripts, and an evaluation report scaffold) and comprehensive test coverage. All changes emphasize reliability, reproducibility, and business value by enabling repeatable, auditable AI ethics benchmarking for policy-relevant decisions.
1 Commits • 1 Features
Feb 1, 2026February 2026 (Month: 2026-02): Delivered MORU Benchmark for AI moral reasoning within UKGovernmentBEIS/inspect_evals. Implemented a modular, type-safe MORU/CAD-Bench framework with dataset loading, scoring criteria, tests, and documentation. Introduced Europa cad_bench components, including EuropaQuestion types and a 35-question dataset, with a memory-dataset factory and HuggingFace-backed loading to improve reproducibility. Completed a major refactor to unify benchmark structure under MORU, including renaming CAPS Bench to MORU and consolidating Cad_Bench assets. Added end-to-end evaluation artifacts (graphs, plotting scripts, and an evaluation report scaffold) and comprehensive test coverage. All changes emphasize reliability, reproducibility, and business value by enabling repeatable, auditable AI ethics benchmarking for policy-relevant decisions.
February 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentPythondata sciencemachine learningtesting
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