ISSN:
ISBN: 978-1-68558-318-7

• Registered: with the Library of Congress of the United States of America (ISSN)
• Free Access: in ThinkMind Digital Library

EXPLAINABILITY 2025 is colocated with the following events as part of TechWorld 2025 Congress:

• EXPLAINABILITY 2025, The Second International Conference on Systems Explainability
• SCALABILITY 2025, The Second International Conference on Systems Scalability and Expandability
• VEHICULAR ANALYTICS 2025, The Second International Conference on Vehicular Systems
• WIRELESS ANALYTICS 2025, The Second International Conference on Wireless and Telecommunications

EXPLAINABILITY 2025 Steering Committee

Thomas Fehlmann Euro Project Office AG, Zurich Switzerland

EXPLAINABILITY 2025 conference tracks:

Concepts for the foundation of explainability
Explainability requirements
Explainability for a diverse audience
Standards to support a device-agnostic cooperation
Explainability via inclusivity, empathy, and emotion adoption
Post hoc explainability
Design guidelines for explainable interfaces
Causality and explainability
Interpretability and understandability
Procedural vs distributive fairness
Fairness, accountability, and transparency
Interpretability methods (predictive accuracy, descriptive accuracy, and relevancy)
Relation: prediction, accuracy, explainability, and trust

Explainability Models
Transparent models for practitioners and users
Unifying approach for interpreting model predictions
Design guidelines for explainable models
Explainable levels vs prediction accuracy of results
Local explanations to global understanding
Intrinsic explainable models
Trustfulness and acceptability models
Model interpretability
Black-box machine learning models (LIME, SHAP)

Classical Explainability Revisited
Improve Product "User's Manual"
Essentials in Drug explanation side effects
Directory of FAQ (Frequently Asked Questions)
Explanatory buyer's contacts
Adverse analytics of laws and governmental decisions
Observability and in-context interpretability
Explainability via social networks
Explainability via validated reputation metrics

Explainability Classical Tools
Interpretation model of product/software predictions
Key Performance Indicators (KPIs)
Repository of data models
Interpretability models
Explainability for human-in-the-middle systems
Cultural context-sensitive social explainability guidelines

Explainable (personalized) Interfaces 
Explainable models for personality
Explainability and social norms
Explainability in personality design
Explainability on emotional interaction
Explainability for tactile and haptic interactions
Explainability for linguistics of personality needs
Explainability for conversational user interfaces (CUIs) (e.g., text-based chatbots and voice-based assistants)
Observable personality
Explainability for impaired users

Explainable Software
Explainability by-design (designer/programmer comments)
Challenges for tracking requirements thru the deployment process
Transparency levels (interface, component, the entire model,  learning algorithms)
Screening methods for deviation and bias (data and algorithms)
Black box vs Explainable box
Insights on model failures/performance
Explainability feature for evaluation of software analytics models
Design for approachability
IF-THEN understanding vs scalability
Metrics and metrology for compliance validation with the requirements

Explainability of Data Processing Algorithms
Classification Prediction accuracy vs Explainability
Deep Learning (Neural Networks)
Support Vector Machines
Ensemble Methods (e.g., Random Forests)
Graphical Models (e.g., Bayesian Networks)
Decision Trees, Classification Rules
Convolutive Neural Networks (for images)

Datasets Explainability
Training datasets vs validation datasets selection explainability
Poor explainability from huge data patterns
Methods for pattern explanation
Explainability on validation algorithms and thresholds selection
Explainability on computation power vs performance trade-off
Post hoc on a dataset (in biostatistics data analytics)
Explaining type-specific topic profiles of datasets
Transformers datasets (for natural language processing model)
Explainability of heterogeneous dataset collections

Personalized Datasets (DS) Explainability
Universal vs. cultural personalized datasets
Sensitive social cues to the cultural context
Ramifications of personality
Observable personality
Explainability for impaired users

Explainability in Small Datasets
Explainability between small data and big data
Statistics on small data
Handling small datasets
Predictive modeling methods for small datasets
Small and incomplete datasets
Normality in small datasets
Confidence intervals of small datasets
Causal discovery from small datasets
Dynamic domain-oriented small datasets (health, sentiment, personal behavior, vital metrics, mobility)

Machine Learning (ML) Explainability
Taxonomy for ML Interpretability
ML Interpretability (ML model accuracy for a valid 'from cause to effect')
ML vs machine personality
Explainabiltiy of opacity and non-intuitiveness models
Explainabiltiy for ML models (supervised, unsupervised, reinforcement, constrained, etc.);
Explainability for generative modeling (Gaussian, HMM, GAN, Bayesian networks, autoencoders, etc.)
Explainability of prediction uncertainty (approximation learning, similarity, quasi-similarity)
Training of models (hyperparameter optimization, regularization, optimizers)
Explanability of data types  (no data, small data, big data, graph data, time series, sparse data, etc.)
Explainability of hardware-efficient machine learning methods
Methods to enhance fairness in ML models

Deep Learning (DL) Explainability
Explainability for Sentiment Analysis
Active learning (partially labels datasets, faulty labels, semi-supervised)
Details on model training and inference
Data Inference for Small/Big Data
Theoretical models for Small/Big Data
(Integrated) Gradients explanation technique
Deep LIFT (deep neural predictions)
Guided BackPropagation, Deconvolution (Convolution Networks)
Class Activation Maps (CAMs), GradCAM, Layer-wise Relevance Propagation (LRP)
RISE algorithm (prediction of Deep Neural Networks for images)

Explainable AI
Large Language Models (LLM)
Autoregressive language models
Limitation of AI-based analytics agents
Visibility into the AI decision-making process
Explainable AI (feature importance, LIME, SHAP, etc.)
Local Interpretable Model-agnostic Explanations (LIME)
Shapley additive explanations (SHAP) (multiple explanations for different kinds of models)
User role-based and system target-based AI explainability

Explainability at work
Lessons learned for deploying explainable models
Limitation self-awareness
Limitation by design (critical missions)
Controlled machine personality
Setting wrong expectations
Wrong (misleading) explainability models
Pitfalls of explainable ML
Missing needs for various stakeholders

AI/ML/DS/DL Explainability tools
Open-source experimental environments
Matching observability perception vs official explainability
Precision model-agnostic explanations
Criticism for interpretability
Fairness-aware ranking
Conflicting explanations
Additive explanations
Counterfactual explanations
Datasets-based tools (e.g., collection faces reacting to robots making mistakes)
Explainability for emerging artificial intelligent partners (robots, chatbots, driverless car transportation systems, etc.)
Bias detection for diversity and inclusion
Small datasets for benchmarking and testing
Small data toolkits
Data summarization

Explainability case studies

• Lessons learned with existing generative-AI tools (ChatGPT, Bard AI, ChatSonic, etc.)
• Sentiment analysis:
  • Explainability DL for sentiment analysis (detection: bias, hate speech, emotions; models)
  • Word-embedding and embedding representations
  • Lexicon-based explainability for sentiment analysis
• Industry AI explainability
  • Predictive maintenance
  • Robot-based production lines
  • Pre-scheduled renewals of machinery
  • Pharmaceutical 
• Output explainability for other case studies
  • Social networks
  • Educational environments
  • Healthcare systems
  • Scholarly discussions (e.g., peer review process discussions, mailing lists, etc.)
  • Mental health systems
  • Human fatigue estimation
  • Hazard prevention

Deadlines:

Deadlines differ for special tracks. Please consult the conference home page for special tracks Call for Papers (if any).