[robocup-worldwide] CFP NIPS workshop on AI and Finances

[Manuela Veloso]

*NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial
Services: the impact of Fairness, Explainability, Accuracy and
Privacy https://sites.google.com/view/feap-ai4fin-2018/
<https://sites.google.com/view/feap-ai4fin-2018/>December 7, 2018Montreal,
CanadaThe 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 papersWe 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 regulationExplainability, including but
not limited to - Explaining credit decisions to customers and regulators-
Regulatory requirements of explainability- Learning interpretable models-
“Debugging” machine learning systemsAccuracy, including but not limited to
- Entity resolution- Missing data- Fraud detection- Credit scoringPrivacy,
including but not limited to - Safe collection and use of consumer and
proprietary data- Secure and private machine learning systems- Responsible
exploratory data analysisWe 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
<https://nips.cc/Conferences/2017/PaperInformation/StyleFiles>. 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
AoEhttps://cmt3.research.microsoft.com/FEAPAI4Fin2018
<https://cmt3.research.microsoft.com/FEAPAI4Fin2018>Key dates: Submission
deadline: Oct 25, 2018 23:59 AoE at
https://cmt3.research.microsoft.com/FEAPAI4Fin2018
<https://cmt3.research.microsoft.com/FEAPAI4Fin2018>Author notification:
Nov 5, 2018Workshop: Dec 7, 2018Organizers: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)*
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