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https://github.com/dell-zhang/awesome-responsible-ai
Research in the area of Responsible AI (machine learning fairness etc.)
https://github.com/dell-zhang/awesome-responsible-ai
List: awesome-responsible-ai
Last synced: 3 months ago
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Research in the area of Responsible AI (machine learning fairness etc.)
- Host: GitHub
- URL: https://github.com/dell-zhang/awesome-responsible-ai
- Owner: dell-zhang
- Created: 2021-06-28T13:09:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-02T20:37:00.000Z (about 3 years ago)
- Last Synced: 2024-04-10T00:11:43.495Z (7 months ago)
- Size: 64.5 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-responsible-ai - Research in the area of Responsible AI (machine learning fairness etc.). (Other Lists / PowerShell Lists)
README
# Responsible AI
A curated list of resources for research in the area of Responsible AI (machine learning fairness, transparency, and accountability etc.)
## Learning Materials
### Reviews
- [Trustworthy AI](https://cacm.acm.org/magazines/2021/10/255716-trustworthy-ai/fulltext)
- [A Survey on Bias and Fairness in Machine Learning](https://arxiv.org/abs/1908.09635)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://arxiv.org/abs/2010.03240)
- [Recommendation Fairness: From Static to Dynamic](https://arxiv.org/abs/2109.03150)
### Tutorials
- [RecSys-21 Tutorial: Bias Issues and Solutions in Recommender System](https://github.com/jiawei-chen/RecDebiasing)
- [SIGIR-21 Tutorial: Fairness of Machine Learning in Recommender Systems](https://fairness-tutorial.github.io/)
- [KDD-20 Tutorial: Dealing with Bias and Fairness in Data Science Systems](https://dssg.github.io/fairness_tutorial/)
- [RecSys-20 Tutorial: Counteracting Bias and Increasing Fairness in Search and Recommender Systems](http://fate.infoseeking.org/resources/RecSys2020_tutorial.pdf)
- [RecSys-19 Tutorial: Fairness and Discrimination in Recommendation and Retrieval](https://fair-ia.ekstrandom.net/recsys2019)
- [FAccT-21 Tutorial: Causal Fairness Analysis](https://fairness.causalai.net/) [(Video)](https://www.youtube.com/watch?v=2xezFQvpJc8)
### Books
- [Fairness and Machine Learning: Limitations and Opportunities](https://fairmlbook.org/)
### Special-Issues
- [SIGKDD Explorations Special Issue on Bias and Fairness in AI](https://kdd.org/explorations/view/june-2021-volume-23-issue-1)
### Papers
- [Papers with Code - Fairness](https://paperswithcode.com/task/fairness)
## Conferences
### Dedicated Conferences
- [FAccT](https://facctconference.org/)
- [AIES](https://www.aies-conference.com/)
### Dedicated Workshops
- [RecSys FAccTRec](https://facctrec.github.io/)
- [SIGIR FACTS-IR](https://fate-events.github.io/facts-ir/)
- [BCS-IRSG Fairness & Bias in IR](https://www.eventbrite.co.uk/e/fairness-and-bias-in-information-retrieval-tickets-129011102681#)
- [ECML/PKDD BIAS](https://sites.google.com/view/bias2021/)
### General AI Conferences
[AI Conference Deadlines](https://aideadlin.es/?sub=ML,CV,NLP,DM)
- [WSDM-22](http://www.wsdm-conference.org/2022/)
- [AAAI-22](https://aaai.org/Conferences/AAAI-22/)
- [ICLR-22](https://iclr.cc/Conferences/2022/)
- [WWW-22](https://www2022.thewebconf.org/)
## Research Groups
### Industrial
- [ByteDance AI Lab - MLF](https://ailab.bytedance.com/research)
- [DeepMind Ethics & Society](https://deepmind.com/about/ethics-and-society)
- [Google AI - Responsible AI](https://ai.google/responsibilities/responsible-ai-practices/)
- [Google Cloud - Responsible AI](https://cloud.google.com/responsible-ai)
- [Microsoft - Responsible AI Resources](https://www.microsoft.com/en-us/ai/responsible-ai-resources)
- [Facebook AI - Responsible AI](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/)
### Academic
- [Alan Turing Institute - Fairness, Transparency, Privacy](https://www.turing.ac.uk/research/interest-groups/fairness-transparency-privacy)
- [TAILOR - European Research Network](https://tailor-network.eu/)
- [Standford - Human-Centered AI - Human Impact](https://hai.stanford.edu/research/research-focus-areas/human-impact-research-mission)
- [UC Berkeley - Human-Compatible AI](https://humancompatible.ai/research)
- [Leverhulme Centre for the Future of Intelligence](http://lcfi.ac.uk/)
- [China-UK Research Centre for AI Ethics and Governance](https://ai-ethics-and-governance.institute/)
## Code and Data
- [Fairlearn](https://fairlearn.org/)
- [AI Fairness 360](https://aif360.mybluemix.net/) & [AI Exaplainability 360](https://aix360.mybluemix.net/)
- [Responsibly](https://docs.responsibly.ai/)
- [ML-Fairness-Gym](https://github.com/google/ml-fairness-gym)