# Tutorial: Safe and Reliable Machine Learning

arXiv: https://arxiv.org/abs/1904.07204 · HTML: https://mlsanity.com/paper/1904.07204/

## Key facts

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

## Built on by (most cited first, 22 of 22)

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- [Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis](https://mlsanity.com/paper/2402.17554/) (2024)
- [Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning](https://mlsanity.com/paper/2404.03310/) (2024)
- [Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection](https://mlsanity.com/paper/2406.15268/) (2024)
- [SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning](https://mlsanity.com/paper/2507.17979/) (2025)

## 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.