NeurIPS 2020 Workshop on Fair AI in Finance
December 11, 2020
December 11, 2020
The financial services industry has unique needs for fairness when adopting artificial intelligence and machine learning (AI/ML). First and foremost, there are strong ethical reasons to ensure that models used for activities such as credit decisioning and lending are fair and unbiased, or that machine reliance does not cause humans to miss critical pieces of data. Then there are the regulatory requirements to actually prove that the models are unbiased and that they do not discriminate against certain groups.
Emerging techniques such as algorithmic credit scoring introduce new challenges. Traditionally financial institutions have relied on a consumer’s past credit performance and transaction data to make lending decisions. But, with the emergence of algorithmic credit scoring, lenders also use alternate data such as those gleaned from social media and this immediately raises questions around systemic biases inherent in models used to understand customer behavior.
The concern for fairness in financial services also requires us to address challenges at different levels: from dataset bias and the design of fair algorithms to the investigation of the complex technical, organizational, and sociological ecosystems in which machine learning models reside. Finally, careful attention needs to be paid to ways in which AI can not only be de-biased, but also how it can play an active role in making financial services more accessible to those historically shut out due to prejudice and other social injustices.
Market fairness is of additional interest. Electronic trading, where a large number of agents interact within short periods of time, provides ample opportunity for the AI tools to be used for unfairness monitoring and detection. AI approaches such as multi-agent simulations can be used by regulators to design more effective policies. Therefore, fairness theory needs to be applied to market paradigms to help quantify domain specific problems and challenges.
The aim of this workshop is to bring together researchers from different disciplines to discuss the challenges for fair AI in financial services, and the opportunities such challenges present to the community. For the first time, four major banks have come together to organize this workshop along with researchers from two universities as well as SEC and FINRA (Financial Industry Regulatory Authority). Our confirmed invited speakers come with different backgrounds including AI, law and cultural anthropology, and we hope that this will offer an engaging forum with diversity of thought to discuss the fairness aspects of AI in financial services ,
We invite short papers on all aspects of fairness in financial services.
Topics include (but not limited to) the following:
Systemic bias and its impact on financial outcomes on different customer segments
Metrics of fairness
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
Market fairness and metrics of market fairness
AI/ML for fair market regulations
Fairness in human-in-the-loop systems
Decisions under uncertainty and fairness
Explainability techniques for investigating bias and fairness
Impacts of decision complexity on fairness outcomes
We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry, and position papers that provide interdisciplinary perspectives:
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, 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.
Position papers: interdisciplinary perspectives and opinions of what AI practitioners in this industry should know about historical, social and economic factors relevant to financial services, to avoid amplifying systemic biases and to be more proactive to build inclusivity into their AI applications.
All submissions must be PDFs formatted in the NeurIPS 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.
Papers should be submitted on CMT3 by Oct 10, 2020 23:59 AoE
https://cmt3.research.microsoft.com/FAIF2020
Submission deadline: Oct 10, 2020 23:59 AoE at https://cmt3.research.microsoft.com/FAIF2020
Author notification: Oct 30, 2020
Workshop: Dec 11, 2020