Summary

The financial services industry has unique needs for robustness when adopting artificial intelligence and machine learning (AI/ML). Many challenges can be described as intricate relationships between algorithmic fairness, explainability, privacy, data management, and trustworthiness. 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 does not cause humans to miss critical pieces of data. The use and protection of customer data necessitates secure and privacy-aware computation, as well as explainability around the use of sensitive data. 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, resulting in large-scale data breaches and loss of confidential information in the financial industry. The need to counteract malicious actors therefore calls for robust methods that can tolerate noise and adversarial corruption of data. However, recent advances in adversarial attacks of AI/ML systems demonstrate how often generic solutions for robustness and security fail, thus highlighting the need for further advances. The challenge of robust AI/ML is further complicated by constraints on data privacy and fairness, as imposed by ethical and regulatory concerns like GDPR.

This workshop aims to bring together researchers and practitioners to discuss challenges for AI/ML in financial services, and the opportunities such challenges represent to research communities. The workshop will consist of invited talks, panel discussions and short paper presentations, which will showcase ongoing research and novel algorithms resulting from collaboration of AI/ML and cybersecurity communities, as well as the challenges that arise from applying these ideas in domain-specific contexts.



Call for Papers - Research, Tutorials and Industry Overviews

We invite short research 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

Trustworthiness, including but not limited to
- Adversarial attacks against ML algorithms
- Trustworthiness metrics for ML systems
- ML robustness guarantees

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
- Differential privacy and other privacy metrics


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.


Submission Guidelines:

All submissions must be PDFs formatted in the NeurIPS 2019 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 September 19, 2019 23:59 PST

https://cmt3.research.microsoft.com/NIPSRAIFS2019