From December 02, 2019 18:44 until December 05, 2019 20:44 Save to calendar

Categories: Conferences , Workshop

Tags: interpretability , methodologies , algorithms

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Important Dates

Manuscript submission

Paper submission: 28th October 2019

Notification: 11th November 2019

Camera-ready version: 18th November 2019

Submission site:

Please, submit your paper via EasyChair Workshop submission site.


Aims and Scope

The First Annual International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019), in conjunction with AI 2019 and AusDM 2019, will provide a joint industry, government and academia forum for presentation and discussion of the latest ideas, research and practical developments and methodologies that address the challenges of interpretability and comprehensibility in machine learning (ML) and broader artificial intelligence (AI). The workshop aims to connect experts in the area of explainable AI, experts in interpretability of machine learning algorithms and experts in data science project methodologies.

The major topics include but are not limited to:

  • The concepts of interpretability, comprehensibility and explainability in machine learning and broader data-driven algorithmic decision making;
  • Degrees of interpretability and respective interpretability features;
  • Methodologies supporting interpretability/comprehensibility in data science projects;
  • Interpretability/comprehensibility as core part of user experience;
  • Interactive and iterative methods supporting interpretability and comprehensibility;
  • Design of interpretable models;
  • Interpretability methods for ‘black-box’ type of machine learning models;
  • Impact of data characteristics on the solution interpretability;
  • Data preprocessing and its effect on interpretability;
  • Interpretability issues across text, image, audio and video data;
  • Interpretability and accuracy;
  • Local and global explainability techniques of AI/ML models;
  • Practical aspects of achieving ML/AI solution interpretability in industry settings;
  • Transparency in machine learning and data-driven decision algorithms;
  • Design of symbolic and visual analytics means for support of interpretability;
  • Psychological and cultural aspects of interpretability/comprehensibility;
  • Causality in predictive modelling and interpretability of causal relationships.
2019-12-02 18:44:00
2019-12-05 20:44:00