mattbit/awesome-ai-safety

A curated list of papers & technical articles on AI Quality & Safety 📚

Awesome AI Safety

Figuring out how to make your AI safer? How to avoid ethical biases, errors, privacy leaks or robustness issues in your AI models?

This repository contains a curated list of papers & technical articles on AI Quality & Safety that should help 📚

Table of Contents

You can browse papers by Machine Learning task category, and use hashtags like #robustness to explore AI risk types.

1. Tabular Machine Learning
2. Natural Language Processing
3. Computer Vision
4. Recommendation System
5. Time Series
6. General ML Testing

Tabular Machine Learning

• Machine Learning Model Drift Detection Via Weak Data Slices (Ackerman et al., 2021) #DataSlice #Debugging #Drift
• Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach (Chung et al., 2020) #DataSlice
• Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (Krause et al., 2016) #Explainability

Natural Language Processing

• Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Ribeiro et al., 2020) #Robustness
• Pipelines for Social Bias Testing of Large Language Models (Nozza et al., 2022) #Bias #Ethics
• Why Should I Trust You?": Explaining the Predictions of Any Classifier (Ribeiro et al., 2016) #Explainability
• A Unified Approach to Interpreting Model Predictions (Lundberg et al., 2017) #Explainability
• Anchors: High-Precision Model-Agnostic Explanations (Ribeiro et al., 2018) #Explanability
• Explanation-Based Human Debugging of NLP Models: A Survey (Lertvittayakumjorn, et al., 2021) #Debugging
• SEAL: Interactive Tool for Systematic Error Analysis and Labeling (Rajani et al., 2022) #DataSlice #Explainability

Large Language Models

• Holistic Evaluation of Language Models (Liang et al., 2022) #General
• Learning to summarize from human feedback (Stiennon et al., 2020) #HumanFeedback

Computer Vision

• DOMINO: Discovering Systematic Errors with Cross-modal Embeddings Domino (Eyuboglu et al., 2022) #DataSlice
• Explaining in Style: Training a GAN to explain a classifier in StyleSpace (Lang et al., 2022) #Robustness
• Model Assertions for Debugging Machine Learning (Kang et al., 2018) #Debugging

Recommendation System

Contributions are welcome 💕

Time Series

Contributions are welcome 💕

General ML Testing

• Machine learning testing: Survey, landscapes and horizons (Zhang et al., 2020) #General
• Quality Assurance for AI-based Systems: Overview and Challenges (Felderer et al., 2021) #General
• Metamorphic testing of decision support systems: A case study (Kuo et al., 2010) #Robustness
• A Survey on Metamorphic Testing (Segura et al., 2016) #Robustness
• Testing and validating machine learning classifiers by metamorphic testing (Xie et al., 2011) #Robustness
• The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction (Breck et al., 2017) #General
• The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective (Krishna et al., 2022) #Explanability
• InterpretML: A Unified Framework for Machine Learning Interpretability (Nori et al., 2019) #Explainability #General