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EXPLAINABILITY 2024 : The First International Conference on Systems Explainability

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Link: https://www.iaria.org/conferences2024/EXPLAINABILITY24.html
 
When Nov 17, 2024 - Nov 21, 2024
Where Valencia, Spain
Submission Deadline Jul 21, 2024
Notification Due Sep 23, 2024
Final Version Due Oct 18, 2024
Categories    datasets   deep learning   systems   AI
 

Call For Papers

INVITATION:

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Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to:

- EXPLAINABILITY 2024, The First International Conference on Systems Explainability

EXPLAINABILITY 2024 is scheduled to be November 17 - 21, 2024 in Valencia, Spain under the TechWorld 2024 umbrella.

The submission deadline is July 21, 2024.

Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org

All events will be held in a hybrid mode: on site, online, prerecorded videos, voiced presentation slides, pdf slides.

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============== EXPLAINABILITY 2024 | Call for Papers ===============

CALL FOR PAPERS, TUTORIALS, PANELS


EXPLAINABILITY 2024, The First International Conference on Systems Explainability

General page: https://www.iaria.org/conferences2024/EXPLAINABILITY24.html

Submission page: https://www.iaria.org/conferences2024/SubmitEXPLAINABILITY24.html


Event schedule: November 17 - 21, 2024


Contributions:

- regular papers [in the proceedings, digital library]

- short papers (work in progress) [in the proceedings, digital library]

- ideas: two pages [in the proceedings, digital library]

- extended abstracts: two pages [in the proceedings, digital library]

- posters: two pages [in the proceedings, digital library]

- posters: slide only [slide-deck posted at www.iaria.org]

- presentations: slide only [slide-deck posted at www.iaria.org]

- demos: two pages [posted at www.iaria.org]


Submission deadline: July 21, 2024


Extended versions of selected papers will be published in IARIA Journals: https://www.iariajournals.org

Print proceedings will be available via Curran Associates, Inc.: https://www.proceedings.com/9769.html

Articles will be archived in the free access ThinkMind Digital Library: https://www.thinkmind.org


The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.

All tracks are open to both research and industry contributions.
Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html


EXPLAINABILITY 2024 Topics (for topics and submission details: see CfP on the site)

Call for Papers: https://www.iaria.org/conferences2024/CfPEXPLAINABILITY24.html

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EXPLAINABILITY 2024 Tracks (topics and submission details: see CfP on the site)


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



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EXPLAINABILITY 2024 Committee: https://www.iaria.org/conferences2024/ComEXPLAINABILITY24.html


Open Access Special Advertising and Publicity Board

Lorena Parra Boronat, Universitat Politecnica de Valencia, Spain

Laura Garcia, Universidad Politécnica de Cartagena, Spain

José Miguel Jimenez, Universitat Politecnica de Valencia, Spain

Sandra Viciano Tudela, Universitat Politecnica de Valencia, Spain

Francisco Javier Díaz Blasco, Universitat Politècnica de València, Spain

Ali Ahmad, Universitat Politècnica de València, Spain

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