Stand with UkraineXAI in Action Workshop

    XAI in Action: Past, Present, and Future Applications

    NeurIPS 2023 Workshop


    About

    As AI models continue to advance in complexity and sophistication, understanding how they work and make decisions is becoming increasingly challenging. This challenge has prompted a surge of research into developing methods and tools that can enhance the transparency and explainability of these models. Nowadays, there are many such methods available, to the point that their specific applications have become somewhat unclear.

    This workshop will specifically explore the diverse applications of explainable artificial intelligence (XAI) methods in various areas. The areas will include, but not limited to XAI in Healthcare, Natural Science, Auditing, Fairness, Natural Language Processing and Law. By examining the use of XAI in these fields, the workshop will provide attendees with insights into the latest trends and challenges within the different domains.

    The workshop discussions aim to delve into the latest advancements in applied XAI and devise ways to further progress the field. The objective is to foster an open and productive dialogue that enhances our understanding of the potential opportunities and constraints of XAI and its impact across different domains. The purpose of this discourse is to identify strategies that can extend the frontiers of applied XAI and make notable progress in this rapidly evolving area. Specifically, the workshop aims to:

    Topics covered

    • Examine various applications of XAI from the past and present
    • Discuss potential applications of XAI in the future
    • Identify the obstacles that hinder progress in each use case and how can we overcome them
    • Explore the necessary methodological requirements for applying XAI
    • Identify new domains where XAI can be useful in the future
    • Understand the inherent limitations of XAI
    • Explore whether insights gained from one use case can be transferred to other use cases

    The workshop will provide a valuable learning opportunity for researchers, practitioners, and students seeking to apply XAI in their work, as it will feature presentations by experts in the field, as well as interactive discussions and insights into the latest trends and future directions in applied XAI. By bringing together a diverse group of participants with a shared interest in XAI, the workshop aims to foster collaboration, innovation, and knowledge sharing in this rapidly growing field.

    Dates

    Note: all deadlines are in Anywhere on Earth.

    Paper Submission

    Submission deadline - September 22 October 2 (23:59 GMT), 2023
    Author notification - October 20 October 25 (23:59 GMT), 2023
    Camera ready deadline - November 10 (23:59 GMT), 2023

    Workshop Event

    Date: December 16, 2023

    Schedule

    To be announced.

    Speakers

    Organisers

    Chhavi Yadav

    Chhavi Yadav

    PhD student at UCSD, her interests lie in XAI, Secure Verification, Auditing and societal impacts of deep generative models
    Michal Moshkovitz

    Michal Moshkovitz

    Machine Learning Research Scientist at Bosch Research, she has been focused on developing the foundations of explainable machine learning
    Bingqing Chen

    Bingqing Chen

    Machine Learning Research Scientist at Bosch Research, her research lies at the intersection of machine learning and energy systems
    Nave Frost

    Nave Frost

    Research Scientist at eBay Research, his research interests focus on supplying explanations for data science applications
    Suraj Srinivas

    Suraj Srinivas

    Postdoctoral research fellow at Harvard University, his research focuses on developing the foundations for interpretable machine learning
    Valentyn Boreiko

    Valentyn Boreiko

    PhD student at the University of Tübingen and a sabbatical student at Bosch Research, his research focuses on development of interpretability technique for vision classifiers
    Hima Lakkaraju

    Hima Lakkaraju

    Assistant Professor at Harvard University who focuses on the algorithmic and applied aspects of explainability, fairness, robustness, and privacy of machine learning models
    Zico Kolter

    Zico Kolter

    Aassociate Professor at CMU, and chief scientist at Bosch Research, his work spans the intersection of machine learning and optimization
    Dotan Di Castro

    Dotan Di Castro

    Research scientist and lab manager at Bosch Research, his research focuses on Reinforcement Learning and Computer Vision
    Kamalika Chaudhuri

    Kamalika Chaudhuri

    Associate Professor at UCSD and a Research Scientist at Meta AI, her research interests lie in the foundations of trustworthy machine learning

    Contact information

    • Email: appliedXAI.neurips2023 [AT] gmail.com
    • Twitter: @XAI_in_Action