mlsanityData to 14 Oct 2026

Tutorial: Safe and Reliable Machine Learning

arXiv 1904.07204 ↗First posted 15 Apr 2019cs.LG cs.AIcited by 22

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Abstract (arXiv)

This document serves as a brief overview of the "Safe and Reliable Machine Learning" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). The talk slides can be found here: https://bit.ly/2Gfsukp, while a video of the talk is available here: https://youtu.be/FGLOCkC4KmE, and a complete list of references for the tutorial here: https://bit.ly/2GdLPme.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2019.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 5; 2024: 5; 2025: 1.

22 papers.

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  1. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 32
  2. 2022Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shiftscited by 16
  3. 2021Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluationcited by 15
  4. 2022Data-SUITE: Data-centric identification of in-distribution incongruous examplescited by 12
  5. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  6. 2023Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domaincited by 5
  7. 2021Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phasescited by 4
  8. 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learningcited by 4
  9. 2023Deployment of Image Analysis Algorithms under Prevalence Shiftscited by 4
  10. 2021Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guaranteescited by 3
  11. 2024Generalization in Healthcare AI: Evaluation of a Clinical Large Language Modelcited by 2
  12. 2021Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learningcited by 1
10 more
  1. 2022DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measurescited by 1
  2. 2023Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fractioncited by 1
  3. 2024DAGnosis: Localized Identification of Data Inconsistencies using Structurescited by 1
  4. 2021Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEscited by 0
  5. 2022Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systemscited by 0
  6. 2023Survey of Trustworthy AI: A Meta Decision of AIcited by 0
  7. 2024Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosiscited by 0
  8. 2024Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learningcited by 0
  9. 2024Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detectioncited by 0
  10. 2025SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learningcited by 0