Human Computation and Crowdsourcing have become ubiquitous in the world of algorithm augmentation and data management. However, humans have various cognitive biases that influence the way they make decisions, remember information, and interact with machines. It is thus important to identify human biases and analyse their effect on complex hybrid systems. On the other hand, the potential interaction with a large pool of human contributors gives the opportunity to detect and handle biases in existing data and systems.
The goal of this symposium is to analyse both existing human biases in hybrid systems, and methods to manage bias via crowdsourcing and human computation. We will discuss different types of biases, measures and methods to track bias, as well as methodologies to prevent and solve bias.
An interdisciplinary approach is often required to capture the broad effects that these processes have on systems and people, and at the same time to improve model interpretability and systems’ fairness.
We will provide a framework for discussion among scholars, practitioners and other interested parties, including industry, crowd workers, requesters and crowdsourcing platform managers. We expect contributions combining ideas from different disciplines, including computer science, psychology, economics and social sciences.
We welcome the submission of research papers and abstracts which describe original work that has not been submitted or currently under review, has not been previously published nor accepted for publication elsewhere, in any other journal or conference.
Submissions of the research papers must be in English, in PDF format, and be in the current CEUR-WS single-column conference format.
< We will follow CEUR-WS guidelines, meet their preconditions and expect to get the proceedings published. However, note that there is no guarantee that our volume will get published at CEUR-WS.
It is also possible to opt-out from publication by sending an email to the organizers.
- We welcome the submission of the following types of contributions:
- Full papers should be at most 10 pages in length (including figures, tables, appendices, and references);
- Short papers should be at most 5 pages in length (including figures, tables, appendices, and references);
- Abstracts should be at most 1 page in length (including figures, tables, appendices, and references), should contain just a title and the abstract, and should detail demos or relevant work or ideas which are under development. They can not contain references.
Topics of interest include (but are not limited to):
- Biases in Human Computation and Crowdsourcing
- Human sampling bias
- Effect of cultural, gender and ethnic biases
- Effect of human in the loop training and past experiences
- Effect of human expertise vs interest
- Bias in experts vs. bias in crowdsourcing
- Bias in outsourcing vs bias in crowdsourcing
- Bias in task selection
- Task assignment/recommendation for reducing bias
- Effect of human engagement on bias
- Responsibility and ethics in human computation and bias management
- Preventing bias in crowdsourcing and human computation
- Creating awareness of cognitive biases among human agents
- Measuring and addressing ambiguities and biases in human annotation
- Human factors in AI
- Using Human Computation and Crowdsourcing for Bias Understanding and Management
- Biases in Human-in-the-loop systems
- Identifying new types of cognitive bias in data or content
- Measuring bias in data or content
- Removing bias in data or content
- Dealing with algorithmic bias
- Fake news detection
- Diversification of sources by means
- Provenance and traceability
- Long-term crowd engagement
- Generating benchmarks for bias management
Timezome: Anywhere on Earth (AoE)
- Full, Short, and Abstract papers due: 1 September 2022 AoE (firm deadline)
- Notifications: 10 September 2022
- Conference: 12, 13, and 14 October 2022
We implement a double-blind review process. Submissions must be anonymous and the submission must be made via EasyChair: https://easychair.org/conferences/?conf=bhcc2022
We are committed to create an equal opportunity environment, without regard to race, gender identity or expression, age, disability, or any other status. For this reason, if you feel that you are in a disadvantaged situation or you require assistance please reach out to us (email@example.com). We’ll be more than happy to help and allow everyone to submit a paper.
We are keen to create a fair working environment for the crowd workers and annotators. For this reason, each submission should clearly state the policies implemented to pursue this aim; each paper should be clear about the amount of work required for an annotator to submit the task, the payment, the time spent by the annotators to finish the task, and all the relevant details aimed at making clear that workers and annotators obtained a fair compensation and treatment for their work.
BHCC 2022 Program
Detailed Program (Rome Timezone, GMT+2)
BHCC 2022 Chairs
Introduction Talk: Biases in Human Computation and Crowdsourcing
Damiano Spina RMIT University
A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-box Systems: A Case Study with COVID-related Searches
Dominik Stammback ETH Zurich
Abstractive Summarization for Explainable Claim Verification
Joel Mackenzie The University of Queensland
Exploring the Variability of Crowdworker Querying Behaviour
Virtual Coffee + Social
Germano Massullo CERN
BOINC - A platform for volunteer computing
Gianluca Demartini The University of Queensland
The Source and The Effect of Biased Human Labels on Machine Learning Decisions
Eddy Maddalena University of Udine
Qrowdsmith: gamification and furtherance incentives to enhance paid microtask crowdsourcing
Davide Ceolin Centrum Wiskunde & Informatica
Explaining Argument-based Information Quality Assessments through Crowdsourcing
Virtual Coffee + Social
David La Barbera
Matt Lease University of Texas at Austin
A Better Way to Measure Annotator Agreement for Complex Tasks
Nirmal Roy TU Delft
Users and Contemporary SERPs: A (Re-)Investigation
Falk Scholer RMIT University
Measurement Scales and Crowd Assessments
Tom Lei Han The University of Queensland
Are Citizen Scientists and Crowd Workers Complementary?
Shaoyang Fan The University of Queensland
Socio-Economic Diversity in Human Annotations
Johanne Trippas RMIT University
Mastering your PhD candidature: Practical Tips
Tim Draws TU Delft
Applying the Cognitive-Biases-in-Crowdsourcing Checklist
Ujwal Gadiraju TU Delft
Using Analogies and Commonsense Knowledge for Intelligible Explanations
Alessandro Checco University of Rome La Sapienza
Online communities, misinformation, and post-truth - a computational social science perspective
Elisa Cavatorta King's College London
Revealing the space for a peace agreement among parties in conflict
Virtual Coffee + Social
Jie Yang TU Delft
Human-In-the-Loop AI: Building Trustworthy AI With People
Jordan Freitas Loyola Marymount University
Navigating Expert and Learner Bias in Crowdsourced Annotation
It will be held as an online event.
Official contact information