[PlanetKR] XAI-FIN-2024: 4th Workshop on Explainable AI in Finance - CFP

[Toni, Francesca]

XAI-FIN-2024: 4th Workshop on Explainable AI in Finance
New York University’s Tandon School of Engineering, at the Brooklyn NY campus
Brooklyn, New York, NY, United States, November 15, 2024
Conference website      https://sites.google.com/view/2024-workshop-explainable-ai
Submission link https://easychair.org/conferences/?conf=xaifin2024
Abstract registration deadline  October 2, 2024
Submission deadline     October 2, 2024
Topics: explainable ai<https://easychair.org/cfp/topic.cgi?tid=642577&a=33387003> artificial intelligence<https://easychair.org/cfp/topic.cgi?tid=670&a=33387003> finance<https://easychair.org/cfp/topic.cgi?a=33387003&tid=27085> trustworthy ai<https://easychair.org/cfp/topic.cgi?a=33387003&tid=18653024>
We welcome submissions to the 4th International Workshop on Explainable AI in Finance to be held on 15th November, 2024. The workshop is co-located with the 5th ACM International Conference on AI in Finance (ICAIF, https://ai-finance.org/), November 14th-17th, 2024, New York City.
Areas of Interest include (but are not limited to)

  1.  Novel developments for existing XAI techniques, including: global methods such as intrinsically interpretable models or surrogate modelling; local methods such as counterfactual explanations, feature attribution and argumentation; information-theoretic methods; and qualitative and quantitative metrics for explanation quality.
  2.  Practical deployment of XAI within financial domains: best practices and lessons learned.
  3.  Novel research at the intersection of XAI and other desiderata of trustworthy AI/ML (e.g., fairness, robustness, and privacy).
  4.  Review papers highlighting important challenges and open problems within XAI for Finance.
  5.  User studies of consumer response to XAI techniques and AI model outputs.
  6.  Novel datasets for use within the XAI in Finance community.
  7.  Discussion on industry areas that are less automated and how best to leverage XAI moving forward.
  8.  Quantitative approaches to financial regulation description and enforcement.
  9.  Explainability Challenges posed by Large Language Models/Foundation Models.

Important Dates

  1.
Submission deadline: 2nd October 2024 (AoE)
  2.
Author notification: 14th October 2024
  3.
Workshop: 15th November 2024

Submission Guidelines
We invite submissions on EasyChair<https://easychair.org/conferences/?conf=xaifin2024> of short papers (4 pages) and long papers (8 pages). There is no limit on the number of pages for references and acknowledgments.
Papers must be formatted according to ACM’s sigconf layout (see here<https://ai-finance.org/submission-instructions-2/> for instructions). Papers must be submitted in pdf format on EasyChair<https://easychair.org/conferences/?conf=xaifin2024> and do not need to be anonymous.
Each paper will get at least two reviews.
The workshop will be non-archival in format, but we are intending to share the accepted papers on the workshop web page.
Organisation
The workshop is co-located with the 5th ACM International Conference on AI in Finance (ICAIF) in New York City.
Workshop Co-chairs

  *   Sanghamitra Dutta, PhD, Assistant Professor in Electrical and Computer Engineering at University of Maryland College Park USA
  *   Andreas Joseph, PhD, Senior Research Economist, Advanced Analytics Division, Bank of England, and Research Fellow at the Data Analytics for Finance and Macro (DAFM) Research Centre at King’s College London
  *   Saumitra Mishra, PhD, Executive Director at J.P. Morgan and Research Director of XAI Center of Excellence at J.P. Morgan.
  *   Francesca Toni, PhD, Professor in Computational Logic at the Department of Computing, Imperial College London and Royal Academy of Engineering / J.P. Morgan Research Chair in Argumentation-based Interactive Explainable AI

Assistant chairs

  *   Fabrizio Russo, PhD Candidate, Imperial College London
  *   Shubam Sharma, PhD, Senior Research Scientist, JPMorgan Chase


====
Prof Francesca Toni
Department of Computing, Imperial College London, UK
http://www.doc.ic.ac.uk/~ft


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