# Reviewing the Need for Explainable Artificial Intelligence (xAI)

arXiv: https://arxiv.org/abs/2012.01007 · HTML: https://mlsanity.com/paper/2012.01007/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research](https://mlsanity.com/paper/2211.15343/) (2022)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions](https://mlsanity.com/paper/2206.03585/) (2022)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [On the Impact of Knowledge Distillation for Model Interpretability](https://mlsanity.com/paper/2305.15734/) (2023)
- [Quality Diversity Evolutionary Learning of Decision Trees](https://mlsanity.com/paper/2208.12758/) (2022)
- [Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities](https://mlsanity.com/paper/2111.02244/) (2021)
- [Improving Interpretability via Regularization of Neural Activation Sensitivity](https://mlsanity.com/paper/2211.08686/) (2022)
- [On the Relationship Between Explanations, Fairness Perceptions, and Decisions](https://mlsanity.com/paper/2204.13156/) (2022)
- [Painting the black box white: experimental findings from applying XAI to an ECG reading setting](https://mlsanity.com/paper/2210.15236/) (2022)
- [Explaining Decisions of Agents in Mixed-Motive Games](https://mlsanity.com/paper/2407.15255/) (2024)
- [Cultural Bias in Explainable AI Research: A Systematic Analysis](https://mlsanity.com/paper/2403.05579/) (2024)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Beyond Single-Feature Importance with ICECREAM](https://mlsanity.com/paper/2307.09779/) (2023)
- [Measuring the (Un)Faithfulness of Concept-Based Explanations](https://mlsanity.com/paper/2504.10833/) (2025)
- [Machine Learning in Transaction Monitoring: The Prospect of xAI](https://mlsanity.com/paper/2210.07648/) (2022)
- [Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal](https://mlsanity.com/paper/2302.03180/) (2023)
- [Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work](https://mlsanity.com/paper/2306.15394/) (2023)
- [QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems](https://mlsanity.com/paper/2309.10293/) (2023)
- [Graph Neural Network and NER-Based Text Summarization](https://mlsanity.com/paper/2402.05126/) (2024)
- [Why Reinforcement Learning in Energy Systems Needs Explanations](https://mlsanity.com/paper/2405.18823/) (2024)
- [Abstraction, Validation, and Generalization for Explainable Artificial Intelligence](https://mlsanity.com/paper/2105.07508/) (2021)
- [Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework](https://mlsanity.com/paper/2211.06561/) (2022)
- [Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective](https://mlsanity.com/paper/2311.02102/) (2023)
- [Fuzzy Logic Function as a Post-hoc Explanator of the Nonlinear Classifier](https://mlsanity.com/paper/2401.14417/) (2024)
- [Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging](https://mlsanity.com/paper/2405.12222/) (2024)
- [The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development](https://mlsanity.com/paper/2408.05239/) (2024)
- [A Framework for Causal Concept-based Model Explanations](https://mlsanity.com/paper/2512.02735/) (2025)
- [Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings](https://mlsanity.com/paper/2606.02049/) (2026)

## Abstract (arXiv)

The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI research addresses the need for explainable AI. We conduct a systematic review of xAI literature on the topic and identify four thematic debates central to how xAI addresses the black-box problem. Based on this critical analysis of the xAI scholarship we synthesize the findings into a future research agenda to further the xAI body of knowledge.