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Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities

Waddah Saeed, Christian Omlin

arXiv:2111.06420v1cs.LGcs.AI

TL;DR

Increasing model complexity and black-box AI models lacking transparency create challenges for deploying AI in critical and sensitive domains. This paper conducts a systematic meta-survey of XAI literature, synthesizing challenges and future research directions from 58 selected papers to guide future exploration.

  • Problem

    Increasing model complexity and black-box AI models lacking transparency challenge AI use in critical and sensitive domains, while XAI evaluation lacks agreed definitions and established approaches.

  • Method

    The paper systematically reviews 58 selected papers and organizes XAI challenges and research directions across general issues and machine-learning life-cycle phases.

  • Results

    The meta-survey identifies XAI challenges and future research directions, presenting them individually in 39 points that can overlap and combine into new research opportunities.

  • Takeaways & Limitations

    The survey provides a guide for future exploration in XAI by consolidating challenges and research directions from the selected literature.

  • Takeaways & Limitations

    The reviewed research includes toy examples or narrowly scoped case studies in explainable reinforcement learning, with limited focus on real-world applications and user testing.

Abstract

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The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that identified challenges and potential research directions in XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey for challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions in XAI and (2) challenges and research directions in XAI based on machine learning life cycle's phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area.

1 Introduction

AI progress has increased the use of complex, opaque models, creating a need for transparency in critical domains. This paper addresses scattered XAI challenges and research directions through a systematic meta-survey.

  • Increasing AI adoption has been accompanied by more complex black-box models that lack transparency.
  • XAI seeks to make AI more transparent and support comprehension, trust, and effective management of AI systems.
  • Existing XAI reviews identify challenges and research directions, but their findings remain scattered.
  • The study presents a systematic meta-survey that organizes XAI challenges and potential research directions as a reference for researchers.

2 Why Explainable AI is Needed?

XAI is needed because black-box decisions can affect people, raise regulatory and social concerns, and create development and industrial challenges. Its need is framed through multiple overlapping perspectives.

  • Black-box systems make consequential decisions without revealing their reasons, including in autonomous vehicles, social networks, and medical systems.
  • Explainability may be unnecessary when errors have no severe consequences or when a system is deeply studied and practically trusted.
  • Regulatory perspective: Regulatory requirements such as the GDPR can give affected users a right to request explanations for algorithmic decisions.
  • Scientific perspective: XAI can reveal knowledge extracted by black-box models, potentially supporting discovery of novel concepts in science.
  • Industrial perspective: Industry faces regulation and user distrust, while XAI may mitigate interpretability–performance trade-offs but increase development and deployment costs.
  • Model’s developmental perspective: XAI can help understand, debug, and improve models affected by limited, biased, adversarial, or otherwise problematic data.
  • End-user and social perspectives: The need for XAI also reflects concerns about model trust and unfair decisions arising from problematic behavior or data.

3 From Explainability to Interpretability

The literature lacks agreement on the meanings of explainability and interpretability. The paper proposes distinguishing them by separating the provision of audience-targeted insights from how well those insights make sense to the audience.

  • Existing XAI literature uses explainability and interpretability inconsistently, with substantial ambiguity remaining between definitions.
  • The paper proposes that explainability provides insights to a targeted audience to fulfill a need.
  • Interpretability is defined as the degree to which provided insights make sense within the targeted audience’s domain knowledge.
  • Explainability therefore involves insights, a targeted audience, and the audience’s need for those insights.
  • Interpretability concerns whether explanations are consistent with audience knowledge and support reasoning about model decisions.
  • The proposed distinction is presented as an initial, imperfect step and is used throughout the paper’s discussion of XAI challenges and research directions.

4 Systematic review planning and execution

The study uses a systematic literature review protocol to identify surveys and other studies reporting XAI challenges or research directions. Searches across major databases and reference lists yielded 58 selected papers.

  • The review asks which XAI challenges and research directions have been reported in existing survey studies.
  • Search terms combine XAI terms such as explainable and interpretable with review and research-direction terms.
  • The search covered Scopus, Web of Science, ScienceDirect, IEEE Xplore, SpringerLink, ACM Digital Library, Google Scholar, and arXiv.
  • Studies were included when they surveyed explainable AI and presented XAI challenges or research directions.
  • The review excluded studies that were not written in English or did not discuss XAI challenges or research directions.
  • The researchers screened titles and abstracts, examined eligible papers in detail, and searched their references for additional studies.
  • 58 papers were selected, with most published in 2020 and ten identified in 2021 before the February 2021 search cutoff.
  • The paper organizes XAI challenges and research directions around general themes and the design, development, and deployment phases of the ML life cycle.

5 Discussion

The discussion organizes XAI challenges and research directions into general issues and three machine-learning life-cycle phases: design, development, and deployment.

  • The paper frames XAI challenges and research directions through general concerns and the design, development, and deployment phases of the ML life cycle.

5.1 General Challenges and Research Directions in XAI

General XAI research challenges include fragmented concepts, weak formalization and evaluation, and insufficiently human-centered development. Future work emphasizes rigorous frameworks, interdisciplinary collaboration, and user-informed explanations.

  • Towards more formalism: XAI literature needs systematic definitions because identical concepts may have different names, while different concepts may share one name.
  • Towards more formalism: Isolated research should be consolidated into generic explainable frameworks that guide end-to-end approaches and support abstraction of data-generating properties.
  • Towards more formalism: Objective and human-centered evaluation metrics remain difficult to establish because XAI lacks agreed definitions and ground-truth explanations.Proposed metrics include reliability, trustworthiness, usefulness, fidelity, complexity, generalizability, and human-centered criteria.
  • Multidisciplinary research: Multidisciplinary research involving psychology, HCI, behavioral sciences, physics, and neuroscience is presented as important for human-centric XAI.
  • Explanations and users: User studies and stakeholder involvement can clarify explanation structure, context, goals, and adaptation to users’ changing needs.Suggested methods include interviews, diary studies, observation, and interactive tools.

5.1.3 Explanations and the nature of user experience and expertise

Explanation methods should reflect users’ expertise, goals, and context rather than assuming a uniform audience. The paper links personalization, stakeholder engagement, and human-understandable concepts to more usable explanations.

  • Users differ in expertise, background, communication style, and expected explanation complexity, but current methods often target technically capable users.
  • Explanation design should identify end-users and clearly define the goals of users, systems, and explanations.
  • User studies can track changing goals and needs, supporting guidelines for long-term systems and more personalized explanations.Examples include diary studies, interviews, and observation.
  • Abstraction and inclusion of end-users in model design can simplify explanations and clarify how outputs will be interpreted in domain-specific settings.The medical domain is given as an example where end-user involvement is especially important.
  • Human-centric explanations should connect unintelligible deep-learning concepts and predictive features to concepts meaningful to domain experts.The paper specifically discusses medical-domain users.
  • Explanations should be tailored to user experience, expertise, goals, and context through stakeholder engagement and system design.

5.1.6 Causal explanations

The paper discusses causal, contrastive, counterfactual, and multimodal explainability as complementary directions for improving human understanding and interaction. It also identifies evaluation, standardization, data-type coverage, and method composition as continuing challenges.

  • Causal explanations: Causal explanations address why predictions occurred rather than only how they were produced, but predictive performance and causality can conflict when confounders are missing.
  • Causal explanations: Causal interpretability remains difficult to evaluate because causal ground truth and verified causal relationships are often absent.
  • Contrastive and counterfactual explanations: Contrastive explanations ask why one event occurred instead of another, whereas counterfactual explanations ask what minimal input changes would produce a contrasting output.
  • Contrastive and counterfactual explanations: Contrastive and counterfactual explanations can improve human-machine interaction and personalize algorithmic explanations, but evaluation methods lack standardization across domains and tasks.
  • Contrastive and counterfactual explanations: Future work should include end-users in explanation evaluation and explore unified causal and non-causal explanatory engines.
  • Non-image, non-text, and heterogeneous data: Sequences, graphs, and spatio-temporal data remain less well explained than image and text data, requiring methods that preserve non-image structure.
  • Non-image, non-text, and heterogeneous data: Heterogeneous-data explainability can combine images and physical parameters, while healthcare applications may require local, global, visual, temporal, and textual explanations.
  • Explainability methods composition: Composing explanation methods could answer different questions jointly and potentially improve explainability, accuracy, and human-interpretable communication.

5.1.10 Challenges in the existing XAI models/methods

Existing XAI methods face computational, representational, evaluation, and communication challenges. Research directions emphasize scalable explanations, richer abstraction, audience-aware language, narrative reasoning, and explicit data-quality communication.

  • Scalability: LIME requires a local model for each case, while Shapley values consider all variable combinations, creating scalability and computational-cost challenges.These costs become problematic when many cases require explanations or problems contain many variables.
  • Attribution: Correlated features complicate attribution, and permutation-based sensitivity analyses can create out-of-distribution points that mislead explanations.Feature dependence can break associations during permutation and distort inferred feature effects.
  • Representation: Heatmaps show relevant features but obscure feature interactions and higher-level concepts, motivating meta-explanations that combine low-level evidence.Pixel-level relevance may not explain objects, scenes, or other abstract concepts understandable to users.
  • Model-based and post-hoc methods: Model-based methods need better accuracy and feature-engineering tools, while post-hoc methods still lack agreed formats and may miss or expose incorrect learned relationships.Future work includes more informative features, improved exploratory analysis, unsupervised techniques, and using prior knowledge to improve predictions.
  • Natural language generation: High-quality explanations should be accurate, useful, understandable, purpose- and audience-specific, structured, and communicative about uncertainty and data quality.Natural-language explanations additionally require reliable evaluation, intuitive handling of vague language, narrative generation, and data-quality communication.
  • Natural language generation: Long medical reports remain difficult because language-generation models can lose coherence after several sentences, requiring stronger decoders and comparison with human reports.Transformer decoders are suggested for capturing longer-range word relationships, while evaluation must compare generated and human-authored reports.

5.1.12 Analyzing models, not data

A future direction in interpretability is to analyze assumption-free black-box models directly rather than relying on restrictive assumptions about data. This agenda also requires rigorous uncertainty communication, reproducibility, and economic analysis.

  • Analyzing models, not data: Interpretable machine learning can analyze how models identify important features and represent relationships, shifting attention from data assumptions toward model behavior.The proposed direction adapts statistical tools such as hypothesis, correlation, and interaction measures to black-box models.
  • Analyzing models, not data: Assumption-based data models can be wrong, difficult to check, inflexible, and less predictive than black-box models when abundant data are available.The paper therefore advocates rewriting statistical analysis tools for assumption-free black-box AI models.
  • Communicating uncertainties: Uncertainty arises inherently in machine-learning models, can be introduced by refinement such as overfitting, and is often omitted by permutation importance and Shapley explanations.Research should quantify uncertainty across model life-cycle phases and explanation methods, then communicate it to users.
  • Time constraints: Explanation timing should match the application, resources, and users: some settings require prompt interpretation, while scientific users may tolerate longer analysis.Efficient explanations can save computing resources and support decisions in time-sensitive environments.
  • Reproducibility: Reproducibility requires open data, methodological and infrastructure descriptions, code sharing, and publication standards that support comparisons.A review of XAI using electronic health records found that reproducibility was not sufficiently emphasized.
  • Economic perspective: Economic research on XAI remains sparse, while explanation costs include design, audit logs, trade-secret exposure, innovation delays, flexibility, security, and privacy.The paper identifies cost estimation, proprietary algorithms, trade secrets, and market evolution as open issues.

5.2 Challenges and Research Directions of XAI in the Design Phase

In the design phase, XAI must address data quality, privacy, security, and sharing before model training begins. Research directions include measurable data-quality communication and privacy-preserving distributed learning.

  • Design phase: Data collection and preparation raise two central design-phase challenges: communicating data quality and sharing data.These challenges arise before the training phase.
  • Communicating data quality: Bias, incompleteness, and incorrectness in training data can degrade AI outcomes and make explanations unreliable across populations and environments.The paper illustrates this boundary with lung-cancer risk prediction trained on American data and applied to Delhi residents.
  • Communicating data quality: Future work should develop practical, measurable ways to identify, improve, and communicate data-quality dimensions and associated risks to users.Potential dimensions include completeness, accuracy, consistency, and fairness, with communication tailored to user type.
  • Privacy and security: Data privacy and security require attention to both sharing raw data and preserving it throughout the AI solution’s use.Explanations depend on the data used to build AI systems, making data handling central to XAI design.
  • Data sharing: Data sharing commonly uses direct raw-data exchange or a central analysis repository, creating a significant challenge for data-driven solutions.Users must also trust that data is protected from unauthorized access and used only within their consent.
  • Privacy-preserving learning: Federated learning avoids moving raw data from devices, but more participating clients increase susceptibility to inference attacks on sensitive training information.Suggested directions include privacy-preserving security assurance, optimal noise bounds, and granular adaptive privacy mechanisms.

5.3 Challenges and Research Directions of XAI in the Development Phase

Development-phase XAI research spans knowledge infusion, training monitoring, debugging, visualization, hybrid modeling, rules extraction, and competency assessment. The common goal is to make model behavior more understandable and improve model development.

  • Knowledge infusion: Incorporating human domain knowledge can guide model architectures and produce more interpretable, informative, task-specific explanations.Expert analysis of important image regions is one proposed route for designing models that mimic domain reasoning.
  • Knowledge infusion: XAI can reveal what models learned from injected knowledge, help adjust knowledge-integration methods, and check whether critical systems follow supplied rules.This is especially relevant to applications such as autonomous driving.
  • Training monitoring: Online training monitoring is needed because deep-learning training can take hours to days, and expert insights could steer training while reducing time and resources.Progressive visual analytics systems can identify stable layers, degenerated filters, unprocessed inputs, and unnecessary architecture components.
  • Debugging: Post-training debugging supports trust by helping discover problems that limit predictions, using techniques such as assertions, security audits, residual analysis, and unit tests.Debugging is presented as important for understanding and improving trained models.
  • Model analysis: Models with similar performance may rely on different features, so explainability provides information that error metrics alone cannot provide for model selection.Performance varies across datasets and tasks, but equivalent performance does not imply equivalent reasoning.
  • Visual analytics: Visual analytics for advanced architectures remains difficult because they contain many layers, complex layer designs, and highly connected structures.ResNet and DenseNet are cited as architectures not yet adequately addressed by existing visual-analytic approaches.
  • Visual analytics: Interpreting many saliency maps requires selecting meaningful subsets of data and studying relationships among samples and subsets.Reducing the number of visualizations can reduce analysis sparsity.
  • Model design: Explanation methods and new deep-learning models are expected to develop together as explanations reveal internal structure and support emergence of new models.Hybrid models likewise aim to combine opaque-model expressiveness with transparent-model semantics.

5.4 Challenges and Research Directions of XAI in the Deployment Phase

Deployment-stage XAI faces intertwined privacy, security, safety, and human-interaction challenges. The surveyed directions emphasize privacy-aware methods, risk-sensitive explanations, interactive interfaces, and explanations connected to system behavior.

  • XAI and privacy: Privacy requirements can conflict with users’ right to explanations, while anonymization does not reliably prevent de-anonymization.Disclosing training data may violate privacy, and greater dataset information can increase re-identification risk.
  • XAI and privacy: Privacy-aware ML should connect machine learning and security research, while explanations should clarify how an individual’s data was used.The paper also identifies difficulties reviewing non-consented data during litigation.
  • XAI and security: XAI deployment must address confidentiality and adversarial attacks, including integrity, availability, and privacy violations.Exposing model inputs and outputs can compromise synthesized knowledge, while adversarial examples can produce multiple security violations.
  • XAI and security: Generative models are proposed for attribution and counterfactual explanations that connect outputs with input variables and support privacy protection.These directions use generative models to reveal output-input relationships and create counterfactuals.
  • XAI and safety: Safety research should communicate uncertainty, develop evolving explanations, and evaluate user-centered risks and error-proneness.Suggested tools include output-confidence techniques, input-region explanations, longitudinal explanations, and evaluation metrics or questionnaires.
  • Interactive explanations: Because static explanations may not produce understanding, interactive and audience-tailored interfaces are needed to support human-machine teaming.Proposed directions include multiple modalities, visual question answering, interaction histories, undoable actions, and user feedback.
  • Explanation generation: Explanation generation should connect an agent’s internal mechanisms with its explanation module and dynamically select and combine relevant explanatory elements.The deployment-phase direction targets coherent explanations for actions or results.

6 Conclusions

The paper presents a systematic meta-survey that distinguishes explainability from interpretability and synthesizes XAI challenges and future research directions from selected studies. It offers 39 combined points while acknowledging limits in keyword coverage, author-level synthesis, and domain coverage.

  • Contributions: The meta-survey proposes a distinction between explainability and interpretability and synthesizes challenges and future research directions from 58 selected papers.The reported findings are organized into 39 points that may overlap across researchers’ backgrounds and interests.
  • Limitations: The authors identify incomplete keyword coverage as a limitation that may exclude some very recent papers.
  • Limitations: Combining findings into 39 points reflects the authors’ perspective rather than listing challenges and directions separately for every paper.
  • Limitations: The survey may omit additional XAI challenges and research directions in domains such as IoT, 5G, and digital forensics.
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