CFP: NIPS Workshop FEAP-AI4Fin 2018 (due 10/25)

jiahao
Tue, 04 Sep 2018 22:35:34 +0000
Machine Learning

I’m happy to announce that the call for participation is now open for
the NIPS 2018 Workshop on Challenges and Opportunities for AI in
Financial Services: the Impact of Fairness, Explainability, Accuracy,
and Privacy.

Key dates:

Submission deadline | Oct 25, 2018 23:59 AoE on CMT3
Author notification | Nov 5, 2018
Workshop | Dec 7, 2018

Summary:

The adoption of artificial intelligence in the financial service
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

Entity resolution

Missing data

Fraud detection

Credit scoring

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.

Submission Guidelines:

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.

Organizing Committee

Isabelle Moulinier, Capital One
Jiahao Chen, Capital One
John Paisley, Columbia
Manuela M. Veloso, CMU and JPMorgan
Nathan Kallus, Cornell Tech
Sameena Shah, S&P Global
Senthil Kumar, Capital One

Program Committee (confirmed so far)

Armineh Nourbakhsh, S&P Global
Dietmar Dorr, Google
Louiqa Raschid, U. Maryland
Quanzhi Li, Alibaba
Xiaojie Mao, Cornell University