# A Survey of the Various Methodologies Towards making Artificial Intelligence More Explainable

arXiv: https://arxiv.org/abs/2311.02291 · HTML: https://mlsanity.com/paper/2311.02291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [Falling Rule Lists](https://mlsanity.com/paper/1411.5899/) (2014)
- [Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition](https://mlsanity.com/paper/2005.02335/) (2020)
- [Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME](https://mlsanity.com/paper/1808.00629/) (2018)

## Abstract (arXiv)

Machines are being increasingly used in decision-making processes, resulting in the realization that decisions need explanations. Unfortunately, an increasing number of these deployed models are of a 'black-box' nature where the reasoning behind the decisions is unknown. Hence, there is a need for clarity behind the reasoning of these decisions. As humans, we would want these decisions to be presented to us in an explainable manner. However, explanations alone are insufficient. They do not necessarily tell us how to achieve an outcome but merely tell us what achieves the given outcome. For this reason, my research focuses on explainability/interpretability and how it extends to counterfactual thinking.