mlsanityData to 7 Oct 2026

AI Explainability 360: Impact and Design

arXiv 2109.12151 ↗First posted 24 Sep 2021cs.LG cs.AIcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 24 Sep 2021.

Builds on

15 of 31 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  4. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  5. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  6. 2017Variational Inference of Disentangled Latent Concepts from Unlabeled Observationscited by 240
  7. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  8. 2018Boolean Decision Rules via Column Generationcited by 79
  9. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  10. 2018Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systemscited by 47
  11. 2019Generalized Linear Rule Modelscited by 34
  12. 2018TED: Teaching AI to Explain its Decisionscited by 33
3 more
  1. 2019Leveraging Latent Features for Local Explanationscited by 21
  2. 2018Improving Simple Models with Confidence Profilescited by 12
  3. 2021On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluationcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 5; 2026: 1.

8 papers.

  1. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  2. 2025EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Modelscited by 2
  3. 2025Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethicscited by 2
  4. 2025From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAIcited by 2
  5. 2024An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systemscited by 0
  6. 2025MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systemscited by 0
  7. 2025Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Modelscited by 0
  8. 2026A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Modelscited by 0