# Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research

arXiv: https://arxiv.org/abs/2211.15343 · HTML: https://mlsanity.com/paper/2211.15343/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 140 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [Towards Explainable Deep Neural Networks (xDNN)](https://mlsanity.com/paper/1912.02523/) (2019)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Explainable artificial intelligence model to predict acute critical illness from electronic health records](https://mlsanity.com/paper/1912.01266/) (2019)
- [Reviewing the Need for Explainable Artificial Intelligence (xAI)](https://mlsanity.com/paper/2012.01007/) (2020)
- [Experiences with Improving the Transparency of AI Models and Services](https://mlsanity.com/paper/1911.08293/) (2019)
- [Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis](https://mlsanity.com/paper/1902.06019/) (2019)
- [Explainable AI, but explainable to whom?](https://mlsanity.com/paper/2106.05568/) (2021)
- [Forecasting AI Progress: A Research Agenda](https://mlsanity.com/paper/2008.01848/) (2020)
- [Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Network](https://mlsanity.com/paper/2104.02173/) (2021)

## Built on by (most cited first, 20 of 20)

- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation](https://mlsanity.com/paper/2402.14978/) (2024)
- [The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations](https://mlsanity.com/paper/2307.00364/) (2023)
- [Why is plausibility surprisingly problematic as an XAI criterion?](https://mlsanity.com/paper/2303.17707/) (2023)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)
- [Human-Centered Evaluation of XAI Methods](https://mlsanity.com/paper/2310.07534/) (2023)
- [Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey](https://mlsanity.com/paper/2309.12177/) (2023)
- [Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations](https://mlsanity.com/paper/2508.00674/) (2025)
- ["It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps](https://mlsanity.com/paper/2605.13261/) (2026)
- [Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models](https://mlsanity.com/paper/2309.06814/) (2023)
- [Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective](https://mlsanity.com/paper/2311.02102/) (2023)
- [Explainable AI: Definition and attributes of a good explanation for health AI](https://mlsanity.com/paper/2409.15338/) (2024)
- [Human Guided Learning of Transparent Regression Models](https://mlsanity.com/paper/2502.15992/) (2025)
- [Direct Preference Optimization for Adaptive Concept-based Explanations](https://mlsanity.com/paper/2505.15626/) (2025)
- [Explanation User Interfaces: A Systematic Literature Review](https://mlsanity.com/paper/2505.20085/) (2025)
- [To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms](https://mlsanity.com/paper/2508.16610/) (2025)
- [Towards a Transparent and Interpretable AI Model for Medical Image Classifications](https://mlsanity.com/paper/2509.16685/) (2025)
- [Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Information](https://mlsanity.com/paper/2603.16918/) (2026)
- [Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2603.21846/) (2026)

## Abstract (arXiv)

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also found five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. In addition, we investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed.