Llm Recommendation Bias Analysis
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Catalog Information
This project analyzes bias in Large Language Model (LLM) recommendation systems by evaluating how LLMs select content for recommendation across multiple dimensions.
Description
The project investigates systematic biases in LLM-based content recommendation by generating recommendations using multiple LLMs, analyzing bias by comparing feature distributions between the full post pool and recommended posts, and quantifying effects using statistical measures and machine learning to identify bias patterns. The analysis pipeline evaluates how LLMs select content for recommendation across multiple dimensions including author demographics, content characteristics, sentiment, and toxicity.
الوصف
يستكشف هذا المشروع التحيزات النظامية في توصيات محتوى Large Language Model (LLM) عن طريق تقييم كيفية اختيار LLMs للمحتوى للتوصية عبر عدة أبعاد، بما في ذلك demographics المؤلفين ، وخصائص المحتوى ، والرأي ، والسيئة.
Novelty
9/10Tags
Technologies
Claude Models
Quality Score
Strengths
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No tests found \u2014 high risk of regressions
- No CI/CD configuration \u2014 manual testing and deployment
- 10 bare except/catch blocks swallowing errors
- 1822 duplicate lines detected \u2014 consider DRY refactoring
- 5 'god files' with >500 LOC need decomposition
Recommendations
- Add a test suite \u2014 start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Replace bare except/catch blocks with specific exception types
Security & Health
Languages
Frameworks
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | A comprehensive pipeline for analyzing bias in Large Language Model (LLM) recommendation systems. This framework evaluates how LLMs select content for recommendation across multiple dimensions including author demographics, content characteristics, sentiment, and toxicity. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Data/ML | data-ml | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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