Machine Learning for Science and Society
Cynthia Rudin and
Kiri L. Wagstaff, guest editors
Call For Papers:
Submissions Due: November 16, 2012
In this special issue, we will showcase papers that address problems
of importance to science and society. Machine learning and data mining
have been used, and will continue to be used, in many important
domains that affect people's lives every day; however, it is becoming
less common in many mainstream machine learning venues to publish work
whose primary goal is to have impact on a new real-world problem. The
collection of papers in this special issue will provide an updated
answer to "what is machine learning good for?" in which impact is the
For many domains in which machine learning presently makes an impact,
it is not necessarily the case that the precise choice of machine
learning algorithm is the key factor for success in the
domain. Choices relating to problem formulation and data
representation sometimes matter far more. Further, there can be
several criteria for success beyond predictive performance, including
the cost of different errors, domain-specific operational constraints,
the interpretability of the system's output, and factors limiting or
enabling domain experts to make use of the results. We seek papers
that address these issues and present lessons that can benefit the
community as a whole.
If you are considering submitting, you may find it useful to look at the guidelines for reviewers in advance.
Papers submitted to this issue may center around:
When preparing manuscripts, authors might find it helpful to consider
the full process of knowledge discovery (KDD or CRISP-DM) including
business understanding, data understanding, data preparation,
modeling, evaluation, and deployment.
- A formal knowledge discovery framework for a class of problems, with an application to a particular problem domain
- Specific, novel applications that have not been previously addressed, or not at this scale
- A demonstration of how machine learning can provide a substantive impact to a scientific or social problem, even if no new machine learning algorithm is proposed
- Elucidating the contributions of key components of an algorithm for knowledge discovery, possibly through removing one component of an algorithm or system at a time, grounded in a concrete problem
- A discussion of how to evaluate machine learning algorithms in real contexts
- Means by which machine learning methods can be infused (adopted by domain experts) and in particular, how to communicate with and motivate domain experts to adopt the method.
It is strongly encouraged, but not required, to have a relevant domain
expert as a co-author.
Example challenges that affect science and/or society:
Papers submitted to this special edition must be scientific, in that
they must contain a message that is potentially useful to future
practitioners, as opposed to simply reporting an anecdotal experience.
Papers that only describe a domain by which they are motivated, then
present an empirical comparison ("bake-off") or a new algorithm as the
main result, are likely to be rejected without review. Submissions
about domains for which there is already a well-established and
long-standing mechanism for success through machine learning are less
likely to be accepted.
- public safety
- medical data mining and public health (improved patient care, medical imaging, medical fraud detection, etc.)
- education and infrastructure in the developing world
- sustainability and the environment (ecology, smart grids, etc.)
- infrastructure for communications and the internet
- commerce and e-commerce
- crime and fraud
- space exploration
The papers for this special edition should be short papers,
approximately 8-12 pages in length. Authors should submit
high-quality, original work that has neither appeared in, nor is under
consideration by, other journals. We aim for a fast turnaround time
for reviews to get decisions out quickly (see the timeline below).
Submissions to the special issue must be submitted like regular
submissions to the journal. Instructions can be
You can download LaTeX style files or a Word template.
We prefer papers that are structured as follows:
- Problem of Interest: Introduce the scientific or social
problem. Why does it matter? Whom does it impact? What is the need?
What are the limitations of current solutions to the problem? Be
quantitative where possible.
- Data Preparation: Describe the data provided by the
originating domain. How did you decide what features to use? What
(reproducible) steps did you have to employ to make the data usable
for machine learning? What new insights did you gain about the domain
or problem as part of this process?
- Machine Learning Technique: Describe the ML approach used
to solve this problem. Provide enough detail for a machine learning
audience to understand the method, but do not make this the
centerpiece of your paper.
- Empirical Results: Describe and justify your chosen
methodology. Include comparisons with appropriate baselines and
simple methods. In addition to common evaluation metrics such as
accuracy or false positive rate, define and employ metrics of
relevance to the problem domain. How does the domain measure success?
The paper MUST include a careful discussion of the
results and what the implications are for the problem of interest.
Papers that simply present a table of results or a "bake-off" without
this analysis may be rejected without review.
- Expert Commentary (optional but highly encouraged): Provide
a domain expert's analysis of the results and their actual or
potential impact to the domain. The expert may or may not be involved
in the project effort.
- Potential (or Actual) Infusion: Include a description of how
the approach, or its output (predictions) is being used, or can be
used, domain experts. What is the path to achieving real impact?
- Lessons for the ML Community: What general lessons can the research
community draw from this result? Be explicit.
If you are considering submitting to the special issue and have
questions regarding the scope or need further information, please do
not hesitate to contact the editors:
Cynthia Rudin and Kiri L. Wagstaff, mlj-at-mlimpact.com
- Authors retain the copyrights to their papers.
- Submissions and reviewing will be handled electronically using
standard procedures for Machine Learning.
- Authors must register with the system before they can submit their
- Authors must select the appropriate Article Type -- SI: ML for Science and Society -- when submitting their manuscripts.
- Accepted papers will be published electronically and citable
immediately (before the print version appears).
|Submit title+abstract (by email):
||November 12, 2012|
|Submission deadline:||November 16, 2012|
|Early submissions are welcomed and will receive an earlier review and response.|
|Decisions (for on-time submisions, estimated):|December 21, 2012 January 4, 2013
|Revisions due:|January 11, 2013 January 25, 2013
|Decisions (estimated):||February 10, 2013|
|Final version due:||March 1, 2013|
|Special issue published:||Summer or fall of 2013|