Connectionists: CFP: Big Data Special Issue on Social and Technical Trade-Offs

[Sorelle Friedler]

*Big Data / *http://www.liebertpub.com/big
Call for Papers: Special Issue on Social and Technical Trade-Offs

Guest Editors:
Solon Barocas / Microsoft Research
danah boyd / Data & Society and Microsoft Research
Sorelle Friedler / Haverford College and Data & Society
Hanna Wallach / Microsoft Research and UMass Amherst

Deadline for manuscript submission: *September 15, 2016*

This special issue on Social and Technical Trade-Offs aims to serve two
main purposes:

   1. To highlight exciting and novel work in machine learning, artificial
   intelligence, data mining, and data science that articulates, examines,
   challenges, and addresses the technical and social trade-offs involved in
   the analysis and interpretation of big data.
   2. To pose practical, grounded, and socially-oriented challenges
for researchers
   in machine learning, artificial intelligence, data mining, and data science
   to motivate and guide their research.


Working with “big data” isn't easy, especially when it involves social data.
Researchers and practitioners must make hard choices when cleaning and
processing data, grapple with biased data sets and missing data, and
evaluate the social and technical trade-offs involved in analysis and
interpretation. What are the ethical implications of these choices? What
happens when we get it wrong? How can we prioritize reproducibility? What
happens when biased data and imperfect methods are combined in unexpected
ways? This special issue will examine the trade-offs that emerge from the
interconnected nature of the social and technical decision-making that lies
at the heart of big data.

We encourage submissions that focus on challenges and questions involving
large-scale social data, and that are deployed (or are in the process of
being deployed) in the real world.

Area of focus include (but are not limited to):

   - Surveillance and privacy
   - Healthcare, medicine, and public health
   - Criminal justice and policing
   - Education and learning
   - Disaster relief
   - Urban planning, housing, and infrastructure
   - Finance, scoring, and insurance
   - Public administration and public policy
   - Autonomous experimentation
   - Targeted advertising

Example questions that are relevant include (but are not limited to):

   - How should we strike a balance between model performance and
   interpretability?
   - How can we formalize social concepts in ways that are amenable to
   machine learning methods? How do these formalizations influence the choice
   of machine learning method?
   - How does uncertainty and noise inherent to real-world data sets affect
   the use of these data sets and the use of results obtained from them via
   machine learning methods?
   - How can we incorporate social and ethical considerations into our
   validation methods and choices? What are the social costs of errors or
   class imbalance and the distribution of those errors across populations?
   What are the social implications of prioritizing false positive rates vs.
   false negative rates?
   - When is it appropriate to collect additional data about minority or
   underrepresented populations? How should we address the need for
    balanced datasets without imposing a “diversity tax?” How should we
   weigh the social and financial associated costs and benefits?
   - What are the social consequences and tradeoffs involved in feature
   selection?
   - We encourage submissions from organizations that may do not typically
   write research papers. In addition to submissions from universities and
   corporations, we welcome submissions from government agencies, nonprofit
   organizations, startups, and foundations.

These submissions might be:

   - Papers that describe and evaluate new and/or existing methods that
   balance social and technical factors in decision-making using or
   surrounding big data.
   - Papers that describe trade-offs that emerged during the design and
   implementation of big data systems in industry, government, or nonprofit
   settings.
   - Position papers that highlight sociotechnical challenges that need to
   be overcome in order to make methods that are suited to responsibly solving
   large-scale social challenges.

Deadline for manuscript submission: September 15, 2016.  Submit here:
http://www.liebertpub.com/manuscript/big

Please address any questions to: bd-tradeoffs at lists.datasociety.net

Big Data is a highly innovative, peer-reviewed journal, provides a unique
forum for world-class research exploring the challenges and opportunities
in collecting, analyzing, and disseminating vast amounts of data, including
data science, big data infrastructure and analytics, and pervasive
computing.

Advantages of publishing in Big Data include:

• Big Data is indexed in Thomson Reuters Emerging Sources Citation Index
• Attractive open access options
• Fast and user-friendly electronic submission
• Rapid, high-quality peer review
• Maximum exposure: accessible in 170 countries worldwide


*A web version of this call is available at: *http://www.datasociety.
net/blog/2016/03/10/big-data-cfp-social-technical-trade-offs/
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