# Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems

arXiv: https://arxiv.org/abs/2508.10806 · HTML: https://mlsanity.com/paper/2508.10806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 117 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 25 of 25)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [HIVE: Evaluating the Human Interpretability of Visual Explanations](https://mlsanity.com/paper/2112.03184/) (2021)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations](https://mlsanity.com/paper/2101.08758/) (2021)
- [Visual Abductive Reasoning](https://mlsanity.com/paper/2203.14040/) (2022)
- [From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interaction](https://mlsanity.com/paper/2405.15051/) (2024)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI](https://mlsanity.com/paper/2204.10152/) (2022)
- [A Study on Multimodal and Interactive Explanations for Visual Question Answering](https://mlsanity.com/paper/2003.00431/) (2020)
- [Explainable Activity Recognition for Smart Home Systems](https://mlsanity.com/paper/2105.09787/) (2021)
- [On the Impact of Explanations on Understanding of Algorithmic Decision-Making](https://mlsanity.com/paper/2302.08264/) (2023)
- [Process Knowledge-infused Learning for Clinician-friendly Explanations](https://mlsanity.com/paper/2306.09824/) (2023)
- [Painting the black box white: experimental findings from applying XAI to an ECG reading setting](https://mlsanity.com/paper/2210.15236/) (2022)
- [Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model](https://mlsanity.com/paper/2404.09917/) (2024)
- [Study on the Helpfulness of Explainable Artificial Intelligence](https://mlsanity.com/paper/2410.11896/) (2024)
- [XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All](https://mlsanity.com/paper/2308.03372/) (2023)
- [Evaluation of Human-Understandability of Global Model Explanations using Decision Tree](https://mlsanity.com/paper/2309.09917/) (2023)
- [Advancing Post Hoc Case Based Explanation with Feature Highlighting](https://mlsanity.com/paper/2311.03246/) (2023)
- [Enhancing Explainability in Mobility Data Science through a combination of methods](https://mlsanity.com/paper/2312.00380/) (2023)
- [I-CEE: Tailoring Explanations of Image Classification Models to User Expertise](https://mlsanity.com/paper/2312.12102/) (2023)
- [MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models](https://mlsanity.com/paper/2404.16174/) (2024)

## Abstract (arXiv)

As AI systems are increasingly deployed to support decision-making in critical domains, explainability has become a means to enhance the understandability of these outputs and enable users to make more informed and conscious choices. However, despite growing interest in the usability of eXplainable AI (XAI), the accessibility of these methods, particularly for users with vision impairments, remains underexplored. This paper investigates accessibility gaps in XAI through a two-pronged approach. First, a literature review of 79 studies reveals that evaluations of XAI techniques rarely include disabled users, with most explanations relying on inherently visual formats. Second, we present a four-part methodological proof of concept that operationalizes inclusive XAI design: (1) categorization of AI systems, (2) persona definition and contextualization, (3) prototype design and implementation, and (4) expert and user assessment of XAI techniques for accessibility. Preliminary findings suggest that simplified explanations are more comprehensible for non-visual users than detailed ones, and that multimodal presentation is required for more equitable interpretability.