[Air-L] CfP Special Issue on Social and Cultural Biases in Information, Algorithms, and Systems

[Robert Jäschke]

*Call for Papers*

Special Issue on “Social and Cultural Biases in Information, Algorithms,
and Systems”
Online Information Review (SSCI journal by Emerald Insight; 2016 Impact
Factor: 1.534)
http://www.emeraldgrouppublishing.com/products/journals/call_for_papers.htm?id=7902


Computer algorithms and analytics play an increasing role in citizens’
lives, as they underlie the popular information services and “smart”
technologies, which are rapidly being adopted across sectors of society,
from transportation to education to healthcare . Algorithms allow the
exploitation of rich and varied data sources, in order to support human
decision-making and/or take direct actions; however, there are
increasing concerns surrounding their transparency and accountability.
There is growing recognition that even when designers and engineers have
the best of intentions, systems relying on algorithmic processes can
inadvertently result in serious consequences in the social world, such
as biases in their outputs that can result in discrimination against
individuals and/or groups of people. Recent cases in the news and media
have highlighted the wider societal effects of data and algorithms, and
have highlighted examples of gender, race and class biases in popular
information access services.

It is important to note the complexity of the problem of social and
cultural biases in algorithmic processes. For instance, recent research
shows that word embeddings, a class of natural language processing
techniques that enable machines to use human language in sensible ways,
are quite effective at absorbing the accepted meaning of words (Caliskan
et al., 2017). These algorithms also pick up on the human biases, such
as gender stereotypes (e.g., associating male names with concepts
related to career, and female names with home/family) and racial
stereotypes (e.g., associating European-/African-American names with
pleasant/unpleasant concepts) embedded in our language use. These biases
are “accurate” in that they are comparable to those discovered when
humans take the Implicit Association Test, a widely used measure in
social psychology that reveals the subconscious associations between the
mental representations of concepts in our memory (Greenwald et al., 1998).

The biases inherent in word embeddings provide a good illustration for
the need to promote algorithmic transparency in information systems.
Word embeddings are extensively used in services such as Web search
engines and machine translation systems (e.g., Google Translate), which
rely on the technique to interpret human language in real time. It may
be infeasible to eradicate social biases from algorithms while
preserving their power to interpret the world, particularly when this
interpretation is based on historical and human-produced training data.
In fact, another way of viewing such unconscious biases is as sources of
‘knowledge diversity’; what one thinks are the true facts of the world,
and how one uses language to describe them, is very much dependent on
local context, culture and intentions. An alternative approach would be
to systematically trace and represent sources of ‘knowledge diversity’
in data sources and analytic procedures, rather than eliminate them
(Giunchiglia et al., 2012). Such approaches would support accountability
in algorithmic systems (e.g., a right to explanation of automated
decisions, which to date has proven very challenging to implement). In
addition, these approaches could facilitate the development of more
“fair” algorithmic processes, which take into account a particular
user’s context and extent of “informedness”  (Koene et al., 2017).

The *purpose* of the special issue is to bring together researchers from
different disciplines who are interested in analysing  and tackling bias
within their discipline, arising from the data, algorithms and methods
they use. The theme of the special issue is social and cultural biases
in information, algorithms, and systems, which includes, but is not
limited to, the following areas:
- Bias in sources of data and information (e.g., datasets, data
production, publications, visualisations, annotations, knowledge bases)
- Bias in categorisation and representation schemes (e.g., vocabularies,
standards, etc.)
- Bias in algorithms (e.g., information retrieval, recommendation,
classification, etc.)
- Bias in the broader context of information and social systems (e.g.,
social media, search engines, social networks, crowdsourcing, etc.)
- Considerations in evaluation (e.g., to identify and avoid bias, to
create unbiased test and training collections, crowdsourcing, etc.)
- Interactions between individuals, technologies and data/information
- Considerations for data governance and policy

As the topic is highly interdisciplinary, we expect that this will be
reflected by the submissions. We intend to invite authors from multiple
disciplines, including data/information science, computer science, the
social sciences, and psychology. The resulting special issue may also be
of great interest to practitioners (e.g., in government, non-profit
organisations, or companies) and educators (e.g., in digital literacy).


*Submission and Publication*

Authors are invited to submit original and unpublished papers. All
submissions will be peer-reviewed and judged on correctness,
originality, significance, quality of presentation, and relevance to the
special issue topics of interest. Submitted papers should not have
appeared in or be under consideration for another journal.
Instructions for authors:
http://emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=oir

Paper submission via https://mc.manuscriptcentral.com/oir
Please select the correct issue to submit to: “Social and Cultural
Biases in Information, Algorithms, and Systems”.



*Important Dates*

- Submission Deadline: October 2018
- First Round Notification: December 2018
- Revision Due Date: February 2019
- Final Notification: April 2019
- Final Manuscript Due Date: June 2019
- Publication Date: July 2018


*Guest Editors*

Dr. Jo Bates, Information School, University of Sheffield, UK
Prof. Paul Clough, Information School, University of Sheffield, UK
Prof. Robert Jäschke, Humboldt-Universität zu Berlin, Germany
Prof. Jahna Otterbacher, Open University of Cyprus
Prof. Kristene Unsworth, Stockton University, New Jersey, USA


-- 
Prof. Dr. Robert Jäschke
Humboldt University Berlin & L3S Research Center Hannover
< https://amor.cms.hu-berlin.de/~jaeschkr/ >>><<< +49 (0)30 2093-70960 >
< World Literature: http://weltliteratur.net/ >< http://bibsonomy.org/ >