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.
We invite submissions on EasyChair 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 for instructions). Papers must be submitted in pdf format on EasyChair 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 webpage.
The workshop is co-located with the 4th ACM International Conference on AI in Finance (ICAIF) in New York City.
See "Accessing the Workshop" tab for instructions on how to join the Workshop.