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Sender: "ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Date: Mon, 20 Apr 2020 09:02:59 +0100
Reply-To: Edgar Meij <[log in to unmask]>
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From: Edgar Meij <[log in to unmask]>
X-cc: KG-BIAS <[log in to unmask]>
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KG-BIAS 2020 – Bias in Automatic Knowledge Graph Construction: A Workshop
at AKBC 2020

UC Irvine, USA – Wed June 24, 2020

https://kg-bias.github.io/

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**************************************************************


### Overview

Knowledge Graphs (KGs) store human knowledge about the world in structured
format, e.g., triples of facts or graphs of entities and relations, to be
processed by AI systems. In the past decade, extensive research efforts
have gone into constructing and utilizing knowledge graphs for tasks in
natural language processing, information retrieval, recommender systems,
and more. Once constructed, knowledge graphs are often considered as “gold
standard” data sources that safeguard the correctness of other systems.
Because the biases inherent to KGs may become magnified and spread through
such systems, it is crucial that we acknowledge and address various types
of bias in knowledge graph construction.


Such biases may originate in the very design of the KG, in the source data
from which it is created (semi-)automatically, and in the algorithms used
to sample, aggregate, and process that data.

Causes of bias include systematic errors due to selecting non-random items
(selection bias), misremembering certain events (recall bias), and
interpreting facts in a way that affirms individuals' preconceptions
(confirmation bias). Biases typically appear subliminally in expressions,
utterances, and text in general and can carry over into downstream
representations such as embeddings and knowledge graphs.


This workshop – to be held for the first time at AKBC 2020 – addresses the
questions: “how do such biases originate?”, “How do we identify them?”, and
“What is the appropriate way to handle them, if at all?”.  This topic is
as-yet unexplored and the goal of our workshop is to start a meaningful,
long-lasting dialogue spanning researchers across a wide variety of
backgrounds and communities.


Topics of interest include, but are not limited to:

* Ethics, bias, and fairness

* Qualitatively and quantitatively defining types of bias

  * Implicit or explicit human bias reflected in data people generate

  * Algorithmic bias represented in learned models or rules

  * Taxonomies and categorizations of different biases

* Empirically observing biases

  * Measuring diversity of opinions

  * Language, gender, geography, or interest bias

  * Implications of existing bias to human end-users

  * Benchmarks and datasets for bias in KGs

* Measuring or remediating bias

  * De-biased KG completion methods

  * Algorithms for making inferences interpretable and explainable

  * De-biasing or post-processing algorithms

  * Creating user awareness on cognitive biases

  * Ethics of data collection for bias management

  * Diversification of information sources

  * Provenance and traceability


### Workshop format

We welcome position papers, short papers, and full papers. Both ongoing and
already published work is welcomed, and we will offer authors the option of
having their paper included in the workshop proceedings. More details
regarding the format and schedule for the workshop will be announced closer
to the workshop date.


### Submission Instructions

Submission files should not exceed 8 pages with additional pages allowed
for references. Reviews are double-blind; author names and affiliations
must be removed. All submissions must be written in English and submitted
as PDF files formatted using the sigconf template:
https://www.acm.org/publications/proceedings-template.


Submissions should be made electronically through
https://easychair.org/conferences/?conf=kgbias2020.


### Important Dates

May 4

KG-BIAS 2020 submission deadline

May 18

KG-BIAS 2020 notification

Jun 22-23

AKBC Conference

Jun 24

KG-BIAS 2020 workshop



### Code of Conduct

Our workshop adheres to all principles and guidelines specified in the ACM
Code of Ethics and Professional Conduct <https://www.acm.org/code-of-ethics>
.


### Organizing committee

* Edgar Meij, Bloomberg

* Tara Safavi, University of Michigan

* Chenyan Xiong, Microsoft Research AI

* Miriam Redi, Wikimedia Foundation

* Gianluca Demartini, University of Queensland

* Fatma Özcan, IBM Research


### Program Committee

* Guillaume Bouchard (Facebook AI)

* Soumen Chakrabarti (IIIT Bombay)

* David Corney (Full Fact)

* Jeff Dalton (University of Glasgow)

* Maarten de Rijke (University of Amsterdam)

* Laura Dietz (University of New Hampshire)

* Djellel Difallah (Wikimedia Foundation)

* Ying Ding (University of Texas at Austin)

* Ujwal Gadiraju (L3S Research Center)

* Faegheh Hasibi (Radboud University)

* Lucie-Aimée Kaffee (University of Southampton and Wikidata)

* Jeff Pan (University of Aberdeen)

* Fabrizio Silvestri (Facebook AI)

* Emine Yilmaz (University College London)


### Contact information

You can find us at https://kg-bias.github.io/ and contact us at
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