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

**URL:** <https://discourse.julialang.org/t/cfp-nips-workshop-feap-ai4fin-2018-due-10-25/14556>\
**Category:** Machine Learning\
**Tags:** conference, cfp\
**Created:** [September 4, 2018, 10:35pm UTC](https://discourse.julialang.org/t/cfp-nips-workshop-feap-ai4fin-2018-due-10-25/14556 "2018-09-04T22:35:34Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![jiahao](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jiahao/32/5767_2.png) [@jiahao](https://discourse.julialang.org/u/jiahao)\
**Post date:** [September 4, 2018, 10:35pm UTC](https://discourse.julialang.org/t/cfp-nips-workshop-feap-ai4fin-2018-due-10-25/14556/1 "2018-09-04T22:35:34Z")

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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](https://sites.google.com/view/feap-ai4fin-2018/).

## Key dates:

| Submission deadline | Oct 25, 2018 23:59 AoE on [CMT3](https://cmt3.research.microsoft.com/FEAPAI4Fin2018) |
| --- | --- |
| 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