# Explainable AI: current status and future directions

arXiv: https://arxiv.org/abs/2107.07045 · HTML: https://mlsanity.com/paper/2107.07045/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 81 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Visualizing and Understanding Neural Models in NLP](https://mlsanity.com/paper/1506.01066/) (2015)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Explaining Recurrent Neural Network Predictions in Sentiment Analysis](https://mlsanity.com/paper/1706.07206/) (2017)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://mlsanity.com/paper/1801.04726/) (2018)
- [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Human-grounded Evaluations of Explanation Methods for Text Classification](https://mlsanity.com/paper/1908.11355/) (2019)
- [Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification](https://mlsanity.com/paper/1909.08856/) (2019)
- [An Interpretable Knowledge Transfer Model for Knowledge Base Completion](https://mlsanity.com/paper/1704.05908/) (2017)
- [CNM: An Interpretable Complex-valued Network for Matching](https://mlsanity.com/paper/1904.05298/) (2019)

## Built on by (most cited first, 30 of 33)

- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [FLEX: Continuous Agent Evolution via Forward Learning from Experience](https://mlsanity.com/paper/2511.06449/) (2025)
- [ESCoT: Towards Interpretable Emotional Support Dialogue Systems](https://mlsanity.com/paper/2406.10960/) (2024)
- [AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning](https://mlsanity.com/paper/2404.06345/) (2024)
- [Representation Engineering for Large-Language Models: Survey and Research Challenges](https://mlsanity.com/paper/2502.17601/) (2025)
- [Tell Me a Story! Narrative-Driven XAI with Large Language Models](https://mlsanity.com/paper/2309.17057/) (2023)
- [Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM](https://mlsanity.com/paper/2207.10226/) (2022)
- [View-based Explanations for Graph Neural Networks](https://mlsanity.com/paper/2401.02086/) (2024)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Towards a Framework for Evaluating Explanations in Automated Fact Verification](https://mlsanity.com/paper/2403.20322/) (2024)
- [Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design](https://mlsanity.com/paper/2309.13957/) (2023)
- [Word Importance Explains How Prompts Affect Language Model Outputs](https://mlsanity.com/paper/2403.03028/) (2024)
- [Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges](https://mlsanity.com/paper/2412.11840/) (2024)
- [Explanation-by-Example Based on Item Response Theory](https://mlsanity.com/paper/2210.01638/) (2022)
- [LUCID: Exposing Algorithmic Bias through Inverse Design](https://mlsanity.com/paper/2208.12786/) (2022)
- [A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy](https://mlsanity.com/paper/2309.00911/) (2023)
- [FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations](https://mlsanity.com/paper/2506.11635/) (2025)
- [Explaining Search Result Stances to Opinionated People](https://mlsanity.com/paper/2309.08460/) (2023)
- [Unveiling The Factors of Aesthetic Preferences with Explainable AI](https://mlsanity.com/paper/2311.14410/) (2023)
- [SAIBench: A Structural Interpretation of AI for Science Through Benchmarks](https://mlsanity.com/paper/2311.17869/) (2023)
- [An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers](https://mlsanity.com/paper/2410.01850/) (2024)
- [SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management](https://mlsanity.com/paper/2510.08948/) (2025)
- [Human-centered XAI for Burn Depth Characterization](https://mlsanity.com/paper/2210.13535/) (2022)
- [Implicit Mixture of Interpretable Experts for Global and Local Interpretability](https://mlsanity.com/paper/2212.00471/) (2022)
- [Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence](https://mlsanity.com/paper/2408.12837/) (2024)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [Trustworthy XAI and Application](https://mlsanity.com/paper/2410.17139/) (2024)
- [AI Enabled User-Specific Cyberbullying Severity Detection with Explainability](https://mlsanity.com/paper/2503.10650/) (2025)
- [AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How](https://mlsanity.com/paper/2504.18044/) (2025)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.