# VitrAI -- Applying Explainable AI in the Real World

arXiv: https://arxiv.org/abs/2102.06518 · HTML: https://mlsanity.com/paper/2102.06518/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 20 of 20)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Generalisation in humans and deep neural networks](https://mlsanity.com/paper/1808.08750/) (2018)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Explaining Explanations in AI](https://mlsanity.com/paper/1811.01439/) (2018)
- [iNNvestigate neural networks!](https://mlsanity.com/paper/1808.04260/) (2018)
- [A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning](https://mlsanity.com/paper/1801.05075/) (2018)
- [Deceptive AI Explanations: Creation and Detection](https://mlsanity.com/paper/2001.07641/) (2020)
- [Balancing the Tradeoff Between Clustering Value and Interpretability](https://mlsanity.com/paper/1912.07820/) (2019)
- [EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis](https://mlsanity.com/paper/2003.01670/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Framework for Generating Artificial Datasets to Validate Absolute and Relative Position Concepts](https://mlsanity.com/paper/2509.18177/) (2025)

## Abstract (arXiv)

With recent progress in the field of Explainable Artificial Intelligence (XAI) and increasing use in practice, the need for an evaluation of different XAI methods and their explanation quality in practical usage scenarios arises. For this purpose, we present VitrAI, which is a web-based service with the goal of uniformly demonstrating four different XAI algorithms in the context of three real life scenarios and evaluating their performance and comprehensibility for humans. This work reveals practical obstacles when adopting XAI methods and gives qualitative estimates on how well different approaches perform in said scenarios.