# AI Explainability 360: Impact and Design

arXiv: https://arxiv.org/abs/2109.12151 · HTML: https://mlsanity.com/paper/2109.12151/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [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)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Variational Inference of Disentangled Latent Concepts from Unlabeled Observations](https://mlsanity.com/paper/1711.00848/) (2017)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Boolean Decision Rules via Column Generation](https://mlsanity.com/paper/1805.09901/) (2018)
- [Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals](https://mlsanity.com/paper/2103.11972/) (2021)
- [Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems](https://mlsanity.com/paper/1806.07552/) (2018)
- [Generalized Linear Rule Models](https://mlsanity.com/paper/1906.01761/) (2019)
- [TED: Teaching AI to Explain its Decisions](https://mlsanity.com/paper/1811.04896/) (2018)
- [Leveraging Latent Features for Local Explanations](https://mlsanity.com/paper/1905.12698/) (2019)
- [Improving Simple Models with Confidence Profiles](https://mlsanity.com/paper/1807.07506/) (2018)
- [On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation](https://mlsanity.com/paper/2106.04753/) (2021)

## Built on by (most cited first, 8 of 8)

- [Auditing and Generating Synthetic Data with Controllable Trust Trade-offs](https://mlsanity.com/paper/2304.10819/) (2023)
- [EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models](https://mlsanity.com/paper/2505.01238/) (2025)
- [Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics](https://mlsanity.com/paper/2506.12365/) (2025)
- [From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI](https://mlsanity.com/paper/2511.08361/) (2025)
- [An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems](https://mlsanity.com/paper/2410.17504/) (2024)
- [MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems](https://mlsanity.com/paper/2508.00300/) (2025)
- [Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Models](https://mlsanity.com/paper/2509.14255/) (2025)
- [A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models](https://mlsanity.com/paper/2607.02782/) (2026)

## Abstract (arXiv)

As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.