Connectionists: Cfp: Transparency and Interpretability in Sequential Models @ ICGI'18

[Matthias Gallé]

The ICGI Steering Committee is calling for proposals on the broad
topic of "*Transparency
and Interpretability in Sequential Models*".

====================
Submission Deadline
====================
May 15, 2018


====================
Call for Proposals
====================

We are requesting position papers on how sequential models should be
evaluated and/or designed for transparency. Proposals should address the
questions of how to produce an explanation for an individual prediction and
how to evaluate the quality of such explanation. Proposals must clearly
describe the context for the proposed approach, including a description of
the type of models to which the proposal applies. We welcome both proposals
that address interpretability of black-box models as well as proposals
tailored to a particular family of models. We also welcome proposals
addressing interpretability in the context of specific applications
involving sequential data, including natural language processing, biology
and software engineering.

====================
Context
====================

The widespread adoption of ML and AI technologies raises ethical, technical
and regulatory issues around fairness, transparency and accountability.
Tackling these issues will require a community-wide effort ranging from the
development of new mathematical and algorithmic tools to the understanding
of the regulatory and ethical aspects of each of these concerns by academic
and industry researchers.

A particular topic of growing interest is the capacity of holding
data-driven algorithms accountable for their decisions. For example, the
upcoming GDPR EU regulations require companies to be fair and transparent
about their use of personal data [4]. This has spurred the interest of the
research community [3] not only to show examples of unfair treatment by
existing algorithms [1], but also to come up with solid measures to
evaluate if an algorithm is fair [2,5] and techniques to embed fairness as
a constraint in machine learning algorithms.

Recently proposed methods to produce explanations for decisions made by
machine learning models include focus on models for fixed-size data, and in
general are not applicable to models involving sequential data.
Interpreting sequential models is an inherently harder because of the
non-locality introduced by memory and the recurrence properties of such
models.


====================
Practical Details
====================

Submissions (max. 6 pages plus references in JMLR format) should be
submitted to the “Transparency and Interpretability” track of ICGI (
https://easychair.org/conferences/?conf=icgi2018), before May, 15th 2018.
Accepted proposals will be presented at a special session during ICGI 2018 (
http://icgi2018.pwr.edu.pl/, Wroclaw, Poland; Sept 5-7)

The ICGI Steering Committee intends the special session to spur development
of a future competition around interpretable sequence models. The ICGI
Steering Committee will invite selected authors of papers presented during
the special session to organize a competition on interpretable sequence
models, for which we are discussing sponsorship.



====================
Programme Committee
====================

Borja Balle Pigem - Amazon Research
Leonor Becerra-Bonache - Jean Monnet University
François Coste - INRIA Rennes
Rémi Eyraud - LIF Marseille
Matthias Gallé - Naver Labs Europe
Jeffrey Heinz - Stony Brooks University
Olgierd Unold - Wroclaw University of Technology
Menno van Zaanen - Tilburg University
Sicco Verwer - Delft University of Technology
Ryo Yoshinaka - Kyoto University



====================
References
====================



[1] for examples see for instance https://fairmlclass.github.io/

[2] Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian.
On the (im)possibility of fairness. arXiv:1609.07236, Sept. 23, 2016

[3] workshops (https://www.fatml.org,
http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/ ), as well
as a long list of smaller events and discussion in ML conferences (
https://www.oii.ox.ac.uk/blog/workshops-on-artificial-intelligence-ethics-and-the-law-what-challenges-what-opportunities/,
https://nips.cc/Conferences/2017/Schedule?showEvent=8734,
https://nips.cc/Conferences/2017/Schedule?showEvent=8744)

[4] Wachter, Sandra, Brent Mittelstadt, and Luciano Floridi. "Why a right
to explanation of automated decision-making does not exist in the general
data protection regulation." International Data Privacy Law 7, no. 2
(2017): 76-99.

[5] Kleinberg, Jon, Sendhil Mullainathan, and Manish Raghavan. "Inherent
trade-offs in the fair determination of risk scores." arXiv preprint
arXiv:1609.05807 (2016).
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