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

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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: 2024-10-29T15:44:13.953Z (about 2 months ago)
• Size: 64.5 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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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)