https://github.com/giskard-ai/awesome-ai-safety
📚 A curated list of papers & technical articles on AI Quality & Safety
https://github.com/giskard-ai/awesome-ai-safety
List: awesome-ai-safety
ai ai-alignment ai-quality ai-safety artificial-intelligence awesome awesome-list computer-vision ethical-ai llm llmops machine-learning ml ml-safety ml-testing mlops model-testing model-validation natural-language-processing robustness
Last synced: 19 days ago
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📚 A curated list of papers & technical articles on AI Quality & Safety
- Host: GitHub
- URL: https://github.com/giskard-ai/awesome-ai-safety
- Owner: Giskard-AI
- License: apache-2.0
- Created: 2023-04-19T13:09:18.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-14T12:42:48.000Z (about 1 month ago)
- Last Synced: 2025-04-23T17:02:07.691Z (about 1 month ago)
- Topics: ai, ai-alignment, ai-quality, ai-safety, artificial-intelligence, awesome, awesome-list, computer-vision, ethical-ai, llm, llmops, machine-learning, ml, ml-safety, ml-testing, mlops, model-testing, model-validation, natural-language-processing, robustness
- Homepage: https://giskard.ai
- Size: 64.5 KB
- Stars: 178
- Watchers: 3
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-ai-safety - 📚 A curated list of papers & technical articles on AI Quality & Safety. (Other Lists / Julia Lists)
README
# Awesome AI Safety [](https://github.com/sindresorhus/awesome)
[](./LICENSE)
[](./CONTRIBUTING.md)
[](https://gisk.ar/discord)
[](https://fosstodon.org/@Giskard)
[](http://hits.dwyl.com/Giskard-AI/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. [General ML Testing](#general-ml-testing)
2. [Tabular Machine Learning](#tabular-machine-learning)
3. [Natural Language Processing](#natural-language-processing)
4. [Computer Vision](#computer-vision)
5. [Recommendation System](#recommendation-system)
6. [Time Series](#time-series)
## General ML Testing
* [Machine learning testing: Survey, landscapes and horizons](https://ieeexplore.ieee.org/abstract/document/9000651/) (Zhang et al., 2020) `#General`
* [Quality Assurance for AI-based Systems: Overview and Challenges](https://arxiv.org/abs/2102.05351) (Felderer et al., 2021) `#General`
* [The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction](https://research.google/pubs/pub46555/) (Breck et al., 2017) `#General`
* [Reliable Machine Learning: Applying SRE Principles to ML in Production [BOOK]](https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/) (Chen et al., 2022) `#Reliability`
* [Metamorphic testing of decision support systems: A case study](https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2009.0084) (Kuo et al., 2010) `#Robustness`
* [A Survey on Metamorphic Testing](https://ieeexplore.ieee.org/document/7422146) (Segura et al., 2016) `#Robustness`
* [Testing and validating machine learning classifiers by metamorphic testing](https://www.sciencedirect.com/science/article/abs/pii/S0164121210003213) (Xie et al., 2011) `#Robustness`
* [The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective](https://arxiv.org/pdf/2202.01602.pdf) (Krishna et al., 2022) `#Explainability`
* [InterpretML: A Unified Framework for Machine Learning Interpretability](https://arxiv.org/abs/1909.09223) (Nori et al., 2019) `#Explainability` `#General`
* [Fair regression: Quantitative definitions and reduction-based algorithms](https://proceedings.mlr.press/v97/agarwal19d.html) (Agarwal et al., 2019) `#Fairness`
* [Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making](https://arxiv.org/abs/1903.10598) (Aghaei et al., 2019) `#Fairness`
* [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://jmlr.org/papers/volume21/20-312/20-312.pdf) (Henderson et al., 2020) `#Environment`
### AI Incident Databases
* [AI Incident Database](https://incidentdatabase.ai/) (Responsible AI Collaborative)
* [AI Vulnerability Database](https://avidml.org/database/) (AVID)
## Tabular Machine Learning
* [Machine Learning Model Drift Detection Via Weak Data Slices](https://arxiv.org/pdf/2108.05319.pdf) (Ackerman et al., 2021) `#DataSlice` `#Debugging` `#Drift`
* [Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach](https://ieeexplore.ieee.org/abstract/document/8713886) (Chung et al., 2020) `#DataSlice`
* [Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models](https://dl.acm.org/doi/abs/10.1145/2858036.2858529) (Krause et al., 2016) `#Explainability`
## Natural Language Processing
* [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](http://homes.cs.washington.edu/~marcotcr/acl20_checklist.pdf) (Ribeiro et al., 2020) `#Robustness`
* [Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization](https://arxiv.org/abs/2305.12782) (Chen et al. 2023)`#Robustness`
* [Pipelines for Social Bias Testing of Large Language Models](https://openreview.net/pdf/8be28761ea130113e3be7747870c434f53e9b309.pdf) (Nozza et al., 2022) `#Bias` `#Ethics`
* [Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://arxiv.org/abs/1602.04938) (Ribeiro et al., 2016) `#Explainability`
* [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874) (Lundberg et al., 2017) `#Explainability`
* [Anchors: High-Precision Model-Agnostic Explanations](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf) (Ribeiro et al., 2018) `#Explainability`
* [Explanation-Based Human Debugging of NLP Models: A Survey](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00440/108932/Explanation-Based-Human-Debugging-of-NLP-Models-A) (Lertvittayakumjorn, et al., 2021) `#Debugging`
* [SEAL: Interactive Tool for Systematic Error Analysis and Labeling](https://arxiv.org/abs/2210.05839) (Rajani et al., 2022) `#DataSlice` `#Explainability`
* [Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00041/43452/Data-Statements-for-Natural-Language-Processing) (Bender and Friedman, 2018) `#Bias`
* [Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques](https://arxiv.org/abs/1901.03116) (Font and Costa-jussà, 2019) `#Bias`
* [On Measuring Social Biases in Sentence Encoders](https://arxiv.org/abs/1903.10561) (May et al., 2019) `#Bias`
* [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://arxiv.org/abs/2110.08193) (Parrish et al., 2022) `#Bias`
* [What Do You See in this Patient? Behavioral Testing of Clinical NLP Models](https://arxiv.org/abs/2111.15512) (Van Aken et al., 2021) `#Bias`
### Large Language Models
* [Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators](https://arxiv.org/abs/2310.07289) (Chen et al., 2023) `#reliability`
* [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) (Liang et al., 2022) `#General`
* [Learning to summarize from human feedback](https://proceedings.neurips.cc/paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf) (Stiennon et al., 2020) `#HumanFeedback`
* [Identifying and Reducing Gender Bias in Word-Level Language Models](https://arxiv.org/abs/1904.03035) (Bordia and Bowman, 2019) `#Bias`
## Computer Vision
* [DOMINO: Discovering Systematic Errors with Cross-modal Embeddings Domino](https://arxiv.org/pdf/2203.14960.pdf) (Eyuboglu et al., 2022) `#DataSlice`
* [Explaining in Style: Training a GAN to explain a classifier in StyleSpace](https://arxiv.org/pdf/2104.13369.pdf) (Lang et al., 2022) `#Robustness`
* [Model Assertions for Debugging Machine Learning](https://ddkang.github.io/papers/2018/omg-nips-ws.pdf) (Kang et al., 2018) `#Debugging`
* [Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure](https://dl.acm.org/doi/abs/10.1145/3306618.3314243) (Amini et al.) `#Bias`
* [Diversity in Faces](https://arxiv.org/abs/1901.10436) (Merler et al.) `#Fairness` `#Accuracy`
## Recommendation System
* [Beyond NDCG: behavioral testing of recommender systems with RecList](https://arxiv.org/abs/2111.09963) (Chia et al., 2021) `#Robustness`
## Time Series
[Contributions are welcome 💕](CONTRIBUTING.md)