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»
Tools and Resources
» Resources
Resources
Addressing Data Bias
Tackling Different Types of Bias in Data Projects
Cautionary Tales
Three pitfalls to avoid in machine learning
(Riley 2019)
Data Science
Academic Data Science Alliance
List of AI and Machine Learning Conferences 2023
*NASA AIML Team
NASA GSFC AI Center of Excellence (AICOE)
*NASA Information, Data, & Analytics Services (IDAS)
NASA Transform-to-Open-Science (TOPS)
National Artificial Intelligence Initiative
Explainable and Interpretable ML
DARPA's explainable AI (XAI) program: A retrospective
(Gunning et al. 2021)
Explaining Explanations: An Overview of Interpretability of Machine Learning
(Leilani et al. 2019)
Interpretability and Explainability in Machine Learning [full Harvard course]
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
(Molnar)
Keras Visualization Toolkit
Marrying Fairness and Explainability in Supervised Learning
(Grabowicz et al. 2022)
The Mythos of Model Interpretability
(Lipton 2018)
OpenXAI
Short course on Explainable AI [Stanford Online 2022]
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (
Rudin 2019)
Ethical AI
NASA Framework for the Ethical Use of Artificial Intelligence (AI) (
McLarney et al. 2021)
Algorithm Watch: AI Ethics Guidelines Global Inventory
AGU Ethical AI Resources
and
AGU Report on Ethical/Responsible AI/ML for Earth and Space Sciences
Carnegie Mellon University Block Center for Technology and Society: Responsible AI
Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental Sciences
(Stall et al. 2023)
NASA SMD Ethical AI Initiative
Responsible AI in the Natural Sciences [CMU Mini-Workshop Recordings]
Supporting Responsible Machine Learning in Heliophysics
(Narock et al. 2022)
Trustworthy ML Initiative (TrustML)
Heliophysics Specific
Curated Reference: AI-ready Space Science Data Sets
Machine Learning in Heliophysics (M. Bobra, J. Mason, et al.)
Python in Heliophysics Community
* Access Restricted