Connectionists: 1st CFP: NIPS 2017 "Transparent and Interpretable Machine Learning in Safety Critical Environments" Workshop

[Alfredo Vellido]

*** apologies for cross-posting ***

FIRST CALL FOR PAPERS
=====================

NIPS 2017 Workshop on Transparent and Interpretable Machine Learning in 
Safety Critical Environments
https://sites.google.com/view/timl-nips2017 
<https://sites.google.com/view/timl-nips2017>

Friday, December 8, 8:00am-6:30pm
Long Beach Convention Center, Long Beach, CA, USA
=======================================

IMPORTANT DATES
Submission deadline: 29th of October, 2017
Acceptance notification: 10th of November, 2017
Camera ready due: 26th of November, 2017
NOTE: beware of registration limitations. Main conference already sold 
out although workshop registrations still available.

SUBMISSION
Through CMT system: see workshop site above
==================================

OVERVIEW
The use of machine learning has become pervasive in our society, from 
specialized scientific data analysis to industry intelligence and 
practical applications with a direct impact in the public domain. This 
impact involves different social issues including privacy, ethics, 
liability and accountability.
In the way of example, European Union legislation, resulting in the 
General Data Protection Regulation (trans-national law) passed in early 
2016, will go into effect in April 2018. It includes an article on 
"Automated individual decision making, including profiling" that, in 
fact, establishes a policy on the right of citizens to receive an 
explanation for algorithmic decisions that may affect them. This could 
jeopardize the use of any machine learning method that is not 
comprehensible and interpretable at least in applications that affect 
the individual.
This situation may affect safety critical environments in particular and 
puts model interpretability at the forefront as a key concern for the 
machine learning community. In such context, this workshop aims to 
discuss the use of machine learning in safety critical environments, 
with special emphasis on three main application domains:
- Healthcare
     Decision making (diagnosis, prognosis) in life-threatening conditions
Integration of medical experts knowledge in machine learning-based 
medical decision support systems
     Critical care and intensive care units
- Autonomous systems
     Mobile robots, including autonomous vehicles, in human-crowded 
environments.
     Human safety when collaborating with industrial robots.
     Ethics in robotics and responsible robotics
- Complainants and liability in data driven industries
     Prevent unintended and harmful behaviour in machine learning systems
     Machine learning and the right to an explanation in algorithmic 
decisions
     Privacy and anonymity vs. interpretability in automated individual 
decision making

We encourage submissions of papers on machine learning applications in 
safety critical domains, with a focus on healthcare and biomedicine. 
Research topics of interest include, but are not restricted to the 
following list:
- Feature extraction/selection for more interpretable models
- Reinforcement learning and safety in AI
- Interpretability of neural network architectures
- Learning from adversarial examples
- Transparency and its impact
- Trust in decision making
- Integration of medical experts knowledge in machine learning-based 
medical decision support systems
- Decision making in critical care and intensive care units
- Human safety in machine learning systems
- Ethics in robotics
- Privacy and anonymity vs. interpretability in automated individual 
decision making
- Interactive visualisation and model interpretabilityrpretability in 
automated individual decision making

ORGANIZERS
Alessandra Tosi, Mind Foundry (UK)
Alfredo Vellido, Universitat Politècnica de Catalunya, UPC BarcelonaTech 
(Spain)
Mauricio Álvarez, University of Sheffield (UK)

SPEAKERS AND PANELLISTS
FINALE DOSHI-VELEZ - Assistant Professor of Computer Science, Harvard
BARBARA HAMMER - Professor at CITEC Centre of Excellence, Bielefeld 
University
SUCHI SARIA - Assistant Professor, Johns Hopkins University
DARIO AMODEI - Research Scientist, OpenAI
ADRIAN WELLER - Computational and Biological Learning Lab, University of 
Cambridge and Alan Turing Institute.

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