[PlanetKR] CALL FOR PAPERS - The First International Workshop on XAI in Finance (XAI-FIN)

[Francesca Toni]

CALL FOR PAPERS

The First International Workshop on XAI in Finance (XAI-FIN) will take 
place virtually on November 3, 2021, as part of the 2021 ACM 
International Conference on AI in Finance (ICAIF). For more information 
and updates, please visit the workshop website at 
https://www.doc.ic.ac.uk/~afr114/ICAIF/index.html

===Important Dates===

Submission deadline: 1 October 2021

Author notification: 15 October 2021

Workshop: 3 November 2021, 8am-12pm EST

===Overview===

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. As such, 
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 explanations

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, regulators and financial experts to discuss the key 
opportunities and focus areas within XAI to face the unique challenges 
in the financial sector. The workshop will include invited talks, 
presentations of accepted papers and panel discussions.

===Topics===

Topics include, but are not limited to, the following:

-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.

-Reviews 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.

===Formatting===

We invite submissions on easychair 
(https://easychair.org/conferences/?conf=xaif21) of short papers (4 
pages plus up to one page for references) and long papers (8 pages plus 
up to one page for references).

Papers must be formatted according to  ACM’s sigconf layout. Papers must be

submitted in pdf format on easychair and do not need to be anonymous.

===Workshop Organizers===

Anupam Dutta, PhD, Professor at Electrical and Computer Engineering 
Department and (by courtesy) Computer Science Department, Carnegie 
Mellon University Silicon Valley

http://www.andrew.cmu.edu/user/danupam/ 
<http://www.andrew.cmu.edu/user/danupam/>

Himabindu Lakkaraju, PhD, Assistant Professor at Harvard University  
with appointments in Business School and Department of Computer Science

https://himalakkaraju.github.io/ <https://himalakkaraju.github.io/>

Daniele Magazzeni, PhD, AI Research Director and Head of the Explainable 
AI Center of Excellence, J.P. Morgan AI Research

https://www.jpmorgan.com/technology/artificial-intelligence 
<https://www.jpmorgan.com/technology/artificial-intelligence>


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

https://www.doc.ic.ac.uk/~ft/ <https://www.doc.ic.ac.uk/~ft/>


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

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://kr.org/pipermail/planetkr/attachments/20210909/71ac8ee3/attachment.htm>