FEAP-AI4Fin 2018 : NIPS 2018 Worksop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
Call For Papers
NIPS 2018 Workshop on
Challenges and Opportunities for AI in Financial Services: the impact of Fairness, Explainability, Accuracy and Privacy
December 7, 2018
The adoption of artificial intelligence in the financial services industry, particularly the adoption of machine learning, presents challenges and opportunities. Challenges include algorithmic fairness, explainability, privacy, and requirements of a very high degree of accuracy. For example, there are ethical and regulatory needs to prove that models used for activities such as credit decisioning and lending are fair and unbiased, or that machine reliance doesn’t cause humans to miss critical pieces of data. For some use cases, the operating standards require nothing short of perfect accuracy.
Privacy issues around collection and use of consumer and proprietary data require high levels of scrutiny. Many machine learning models are deemed unusable if they are not supported by appropriate levels of explainability. Some challenges like entity resolution are exacerbated because of scale, highly nuanced data points and missing information. On top of these fundamental requirements, the financial industry is ripe with adversaries who purport fraud and other types of risks.
The aim of this workshop is to bring together researchers and practitioners to discuss challenges for AI in financial services, and the opportunities such challenges represent to the community. The workshop will consist of a series of sessions, including invited talks, panel discussions and short paper presentations, which will showcase ongoing research and novel algorithms.
Call for papers
We invite short papers in the following areas:
Fairness, including but not limited to
Auditing the disparate impact of credit decisioning and lending
Theories of equal treatment and impact
Understanding and controlling machine learning biases
Enforcing fairness at training time
The relationship between fairness theory and fair lending regulation
Explainability, including but not limited to
Explaining credit decisions to customers and regulators
Regulatory requirements of explainability
Learning interpretable models
“Debugging” machine learning systems
Accuracy, including but not limited to
Privacy, including but not limited to
Safe collection and use of consumer and proprietary data
Secure and private machine learning systems
Responsible exploratory data analysis
We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry:
Overview of Industry Challenges: Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
Algorithmic Tutorials: Short tutorials from academic researchers that explain current solutions to challenges related to fairness, explainability, accuracy and privacy, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.
All submissions must be PDFs formatted in the NIPS style. Submissions are limited to 8 content pages, including all figures and tables but excluding references. Despite this page limit, we also welcome and encourage short papers (2-4 pages) to be submitted. All accepted papers will be presented as posters; some may be selected for highlights or contributed talks, depending on schedule constraints. Accepted papers will be posted on the workshop website or, at the authors’ request, may be linked to on an external repository such as arXiv.
Papers should be submitted on CMT3 by Oct 25, 2018 23:59 AoE
Submission deadline: Oct 25, 2018 23:59 AoE at https://cmt3.research.microsoft.com/FEAPAI4Fin2018
Author notification: Nov 5, 2018
Workshop: Dec 7, 2018
Manuela M. Veloso (CMU, JPMorgan Chase)
Nathan Kallus (Cornell)
Senthil Kumar (Capital One)
Sameena Shah (S&P Global)
Isabelle Moulinier (Capital One)
John Paisley (Columbia)
Jiahao Chen (Capital One)