[Liste-proml] Cfp: Transparency and Interpretability in Sequential Models @ ICGI'18
francois.coste at inria.fr
Mer 28 Mar 12:08:17 CEST 2018
Ci-dessous, un appel pour une session originale à ICGI'18 largement
ouvert à ceux qui sont s'intéressés par l'apprentissage sur les
séquences et l'interprétabilité des modèles...
[Please accept our apologies if you receive multiple copies of this Call
for Papers (CFP)]
*The ICGI Steering Committee is calling for proposals on the broad topic
of "*Transparency and Interpretability in Sequential Models*".
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.
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 . This has spurred the
interest of the research community  not only to show examples of
unfair treatment by existing algorithms , 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
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.
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
 for examples see for instancehttps://fairmlclass.github.io/
 Sorelle A. Friedler, Carlos Scheidegger, and Suresh
Venkatasubramanian. On the (im)possibility of fairness.
arXiv:1609.07236, Sept. 23, 2016
well as a long list of smaller events and discussion in ML conferences
 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.
 Kleinberg, Jon, Sendhil Mullainathan, and Manish Raghavan. "Inherent
trade-offs in the fair determination of risk scores." arXiv preprint
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