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1NeurIPS 2022 Gaze Meets ML Workshop
https://groups.google.com/g/ml-news/c/F0JgTZQL-Ns/m/H95PMQyTAgAJ?pli=1
2 years ago by @becker
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1Healthcare Algorithms Don’t Always Need to Be Generalizable
A Stanford researcher questions the need for generalizable models and proposes instead sharing recipes for creating useful local models.
2 years ago by @becker
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1microsoft/responsible-ai-toolbox: This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
https://github.com/microsoft/responsible-ai-toolbox
3 years ago by @becker
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1Google's AI language model Reformer can process the entirety of novels | VentureBeat
https://venturebeat.com/2020/01/16/googles-ai-language-model-reformer-can-process-the-entirety-of-novels/#fromHistory
4 years ago by @schwemmlein
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1How to make a racist AI without really trying | ConceptNet blog
http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/
4 years ago by @schwemmlein
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1Responsible AI: The importance of addressing bias in artificial intelligence
https://www.luminoso.com/post/responsible-ai
4 years ago by @schwemmlein
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12020-2021 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics
2020-2021 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics
4 years ago by @schwemmlein
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
5 years ago by @becker
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1(6) 8 AI Technologies that ain't Neural Networks | LinkedIn
https://www.linkedin.com/pulse/8-ai-technologies-aint-neural-networks-brandon-wirtz/
7 years ago by @becker
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2Machine learning techniques for accountability
B. Kim, and F. Doshi-Velez. AI Magazine, 42 (1): 47--52 (2021)
2 years ago by @becker
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1Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, and S. Ji. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2022)
2 years ago by @becker
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2The Deskilling of Domain Expertise in AI Development
N. Sambasivan, and R. Veeraraghavan. CHI Conference on Human Factors in Computing Systems, page 1--14. (2022)
2 years ago by @becker
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1A biomedical open knowledge network harnesses the power of AI to understand deep human biology
S. Baranzini, K. Börner, J. Morris, C. Nelson, K. Soman, E. Schleimer, M. Keiser, M. Musen, R. Pearce, T. Reza and 1 other author(s). Ai Magazine, 43 (1): 46--58 (2022)
2 years ago by @becker
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4Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet. Information, 11 (4): 193 (2020)
3 years ago by @becker
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1AI in health and medicine
P. Rajpurkar, E. Chen, O. Banerjee, and E. Topol. Nature Medicine, (2022)
3 years ago by @becker
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3Explaining by removing: A unified framework for model explanation
I. Covert, S. Lundberg, and S. Lee. Journal of Machine Learning Research, 22 (209): 1--90 (2021)
3 years ago by @becker
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1AI in health: state of the art, challenges, and future directions
F. Wang, and A. Preininger. Yearbook of medical informatics, 28 (01): 016--026 (2019)
3 years ago by @becker
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5Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
A. Ross, M. Hughes, and F. Doshi-Velez. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, page 2662--2670. (2017)
4 years ago by @schwemmlein
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1Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence
L. Davidson, and M. Boland. Journal of Pharmacokinetics and Pharmacodynamics, (2020)
4 years ago by @becker
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1NeurIPS 2022 Gaze Meets ML Workshop

1Healthcare Algorithms Don’t Always Need to Be Generalizable

1microsoft/responsible-ai-toolbox: This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

1Google's AI language model Reformer can process the entirety of novels | VentureBeat

1How to make a racist AI without really trying | ConceptNet blog

1Responsible AI: The importance of addressing bias in artificial intelligence

12020-2021 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics

4Interpretable Machine Learning

1(6) 8 AI Technologies that ain't Neural Networks | LinkedIn

publications (hide)10
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2Machine learning techniques for accountability

1Explainability in graph neural networks: A taxonomic survey

2The Deskilling of Domain Expertise in AI Development

1A biomedical open knowledge network harnesses the power of AI to understand deep human biology

4Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

1AI in health and medicine

3Explaining by removing: A unified framework for model explanation

1AI in health: state of the art, challenges, and future directions

5Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

1Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

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