https://github.com/jphall663/hc_ml
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
https://github.com/jphall663/hc_ml
accountability data-mining data-science explainable-ai explainable-ml fairness fairness-ai fairness-ml fatml iml interpretability interpretable-ai interpretable-machine-learning interpretable-ml machine-learning machine-learning-interpretability transparency xai
Last synced: 19 days ago
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Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
- Host: GitHub
- URL: https://github.com/jphall663/hc_ml
- Owner: jphall663
- License: cc-by-4.0
- Created: 2019-06-04T19:04:14.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-19T23:40:53.000Z (about 6 years ago)
- Last Synced: 2025-05-30T08:35:42.565Z (9 months ago)
- Topics: accountability, data-mining, data-science, explainable-ai, explainable-ml, fairness, fairness-ai, fairness-ml, fatml, iml, interpretability, interpretable-ai, interpretable-machine-learning, interpretable-ml, machine-learning, machine-learning-interpretability, transparency, xai
- Language: TeX
- Homepage:
- Size: 34.4 MB
- Stars: 22
- Watchers: 3
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Roadmap: roadmap.xml
Awesome Lists containing this project
README
# Toward Responsible Machine Learning
_Toward Responsible Machine Learning_ presentation from various venues.
### Potentially Useful Artifacts
* [Slides](main.pdf)
* [Editable Blueprint Draw.io XML](blueprint.xml)
* [Blueprint Image](img/blueprint.png):
### Videos from Talks:
* [H2O World 2019](https://www.youtube.com/watch?v=diMSemHRNDw)
* [Spark AI Summit 2019](https://databricks.com/session/interpretable-ai-not-just-for-regulators)
* [BDAEDCON 2019](https://www.youtube.com/watch?v=YUi1LRCWxds)
* [CrunchConf 2019](https://www.youtube.com/watch?v=OmGZu3eIvAc)
### Related Papers:
* [On the Art and Science of Explainable Machine Learning](https://arxiv.org/abs/1810.02909)
* [Guidelines for Responsible Use of Explainable Machine Learning](https://arxiv.org/pdf/1906.03533.pdf)