[PlanetKR] ACM ICAIF 2022 Workshop on Explainable AI in Finance (XAIFIN2022) - first call for papers

[Francesca Toni]

ACM ICAIF 2022 Workshop on Explainable AI in Finance (XAIFIN2022)- 2 
November 2022

https://sites.google.com/view/2022-workshop-explainable-ai/


We welcome submissions to the 2nd International Workshop on Explainable 
AI in Finance (XAIFIN2022).
The workshop will be co-located with the 3rd ACM International 
Conference on AI in Finance (New York City and online).


===Important Dates===

  Submission deadline: 28th August 2022, 23:59 (anywhere on earth)

  Author notification: 12th September 2022

  Workshop registration: TBC

  Workshop: 2nd November 2022 (half day)


===Submission information===
We invite submissions 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 the 
instructions at https://ai-finance.org/submission-instructions-2/). 
Papers must be
submitted in pdf format on EasyChair  at 
https://easychair.org/conferences/?conf=xaifin2022 and do not need to be 
anonymous.

The workshop will be non-archival in format, but we are intending to 
share the accepted papers on the workshop webpage.


===Motivation===
Explainable AI (XAI) forms an increasingly critical component of 
operations undertaken within the financial industry, brought about by 
the growing sophistication of state-of-the-art AI models and the demand 
that these models be deployed in a safe and understandable manner.

The financial setting brings unique challenges to XAI due to the 
consequential nature of decisions taken on a daily basis. There are two 
encompassing dimensions to this: macro-financial stability and consumer 
protection. Financial markets transfer enormous amounts of assets on a 
daily basis. AI-powered automation of a substantial fraction of these 
transactions, especially by big players in key markets, poses a risk to 
financial stability if the underlying mechanisms driving market-moving 
decisions are not well understood. This may trigger a crisis-risking 
meltdown in the worst-case scenario.

At the same time, and just as important as macro-stability, is consumer 
protection. Automation within the financial sector is tightly regulated: 
in the US consumer credit space, the Equal Credit Opportunity Act 
(ECOA), as implemented by Regulation B, demands that explanations be 
provided to consumers for any adverse action by a creditor; in the EU, 
consumers have the right to demand meaningful information for automated 
decisions under the General Data Protection Regulation (GDPR). Safe and 
effective usage of AI within finance is thus contingent on a strong 
understanding of theoretical and applied XAI.

Currently, there is no industry standard consensus on which XAI 
techniques are appropriate to use within the different parts of the 
financial industry – or if indeed the current state-of-the-art is 
sufficient to satisfy the needs of all stakeholders. This workshop aims 
to bring together academic researchers, industry practitioners and 
financial experts to discuss the key opportunities and focus areas 
within XAI – both in general and to face the unique challenges in the 
financial sector.

Example Topics of Interest
=========================

-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.

-Practical deployment of XAI within financial domains: best practices 
and lessons learned.

-Novel research at the intersection of XAI and other desiderata of 
trustworthy AI/ML (e.g., fairness, robustness, and privacy).

-Review papers highlighting important challenges and open problems 
within XAI for Finance.

-User studies of consumer response to XAI techniques and AI model outputs.

-Novel datasets for use within the XAI in Finance community.

-Discussion on industry areas that are less automated and how best to 
leverage XAI moving forward.

-Quantitative approaches to financial regulation description and 
enforcement.


===Workshop Co-Chairs===

Xia (Ben) Hu, PhD, Associate Professor in Computer Science at Rice 
University

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, Vice President/AI Research Lead at J.P. Morgan. 
Member 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

Adrian Weller, PhD, Principal Research Fellow in Machine Learning at the 
University of Cambridge and Programme Director for AI at The Alan Turing 
Institute

Additional Information
======================
workshop web site: 
https://sites.google.com/view/2022-workshop-explainable-ai/
easychair call for papers: https://easychair.org/cfp/xaifin2022

-- 
Prof Francesca Toni
Department of Computing
Imperial College London
www.doc.ic.ac.uk/~ft/