XAI-FIN: THE FIRST INTERNATIONAL WORKSHOP ON EXPLAINABLE AI IN FINANCE (NOVEMBER 3, 2021)
The First International Workshop on Explainable AI in Finance (XAI-FIN) will take place virtually on November 3, 2021, 8am-12pm EST, as part of the 2021 ACM International Conference on AI in Finance (ICAIF).
Explainable AI (XAI) is 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 U.S. 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 include, but are not limited to, the following:
- Novel develepments 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.
Submissions through easychair are invited for short papers (4 pages plus up to one page for references) and long papers (8 pages plus up to one page for references). Papers, which do need to be anonymous, must be formatted according to ACM’s sigconf layout and submitted in pdf format. Submission deadline: 1 October 2021; Author notification: 15 October 2021.
Anupam Dutta, PhD, Professor at Electrical and Computer Engineering Department and (by courtesy) Computer Science Department, Carnegie Mellon University Silicon Valley.
Himabindu Lakkaraju, PhD, Assistant Professor at Harvard University with appointments in Business School and Department of Computer Science.
Daniele Magazzeni, PhD, AI Research Director and Head of the Explainable AI Center of Excellence, J.P. Morgan AI Research.
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.
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