The 6th IEEE Workshop on
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| About | Keynotes | Dates and Submission | Program | Organization | HMData 2021 | IEEE Bigdata 2022 |
About IEEE HMData 2022Overview
HMData workshop, which originally started as the "Human-Machine collaboration in BigData" workshop, will investigate the opportunities and challenges in human machine collaboration in work with bigdata, which are described by two terms: Human-in- the-Loop Methods and Future of Work. Human-in-the-Loop is a term focusing on the employer's viewpoint while Future of Work focuses more on worker's viewpoint, in both of which the division of labor among humans and machines is a key issue.
This area is likely to be heavily AI driven, and we intend to invite papers covering the following aspects, (a) Capturing human capabilities through intelligent models and how to adapt them through changing perceptions, needs, and skills. (2) High level tools that provide the ability for all stakeholders in the new ecosystem, including regulators for policies and AI workers, to specify their requirements. (3) system design and engineering of job platforms for collection, storage, retrieval, and analysis of data deluge about workers, jobs, and their activities. (4) Benchmarking and the development of appropriate metrics to measure system performance as well as human aspects, such as satisfaction, capital advancement, and equity.
We welcome any interesting ideas and results on any relevant topics, but this workshop encourages submitting papers the results of which have been or will be implemented as platforms, tools and libraries. This year, we plan to have a thematic session on improving the interoperability of tools on Human-in-the-loop Methods and Future-of-Work. We also solicit practitioner papers as well as research papers, in order to facilitate discussion among researchers have solutions and practitioners who know problems. All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference. Journal publication of selected papers
After the conference, high quality papers will be selected and recommended for possible publication in a special issue of Information Systems Frontiers, Springer.
Topics
This workshop covers a wide range of topics of human-machine collaboration in work with bigdata. Keywords include: crowdsourcing, collaborative recommendation, crowdsensing, workflow model for humans and machines, incentives, human-assisted bigdata analysis, bigdata-human interaction, human-machine collaboration in real-world applications (such as natural disaster response, education, and citizen science), and ELSI in Human-in-the-loop systems and Future of Work.
We expect submissions to address some of the following issues:
Keynotes
Mitigating Biases in Crowdsourcing Data Collection Ming Yin (Purdue University) 
Abstract: Data has become the secret sauce for the rapid progress of artificial intelligence (AI).
Over the past decade, crowdsourcing has become a prevalent paradigm for obtaining data from people to
enhance machine intelligence. However, there is a growing line of literature showing that data collected
from crowdsourcing efforts could have significant biases, which not only decreases the quality of the data,
but may even negatively impact the downstream algorithmic models built based on these data.
Many factors could contribute to the biases in crowdsourced data, including the composition of the dataset
on which annotations are solicited from the crowd (e.g., sampling bias of the dataset), and the cognitive
and behavioral limitations that the crowd is subject to when providing annotations (e.g., cognitive bias,
affective bias, social bias). In this talk, I'll present some of our recent efforts in mitigating biases
throughout the crowdsourcing data collection lifecycle.
Bio: Ming Yin is an Assistant Professor in the Department of Computer Science, Purdue University. Her research broadly connects to the fields of human-computer interaction, applied artificial intelligence and machine learning, computational social science, and behavioral sciences. She uses both experimental and computational approaches to examine how to better utilize the wisdom of crowd to enhance machine intelligence (i.e., crowdsourcing and social computing), and how to better design intelligent systems that people can understand, trust and engage with effectively (i.e., human-AI interaction). Prior to Purdue, She spent a year at Microsoft Research New York City as a postdoctoral researcher in the Computational Social Science group. She completed her Ph.D. in Computer Science at Harvard University, and received her bachelor degree from Tsinghua University, Beijing, China. ProgramTBA
Important Dates
SubmissionAll submissions must be submitted electorically through the submission page (This will be open in August). Please prefix your submission category such as [Research Paper] to the Title of Paper field in the submission page. For example, if you would like to submit a project-in-progress paper "Crowd-centric Approach to Digital Archive Maintenance," you have to put "[project-in-progress paper] Crowd-centric Approach to Digital Archive Maintenance" into the Title of Paper field.All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference. Submission Categories
FormatPapers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines in the IEEE Bigdata 2022 CFP pageOrganizationChairsSenjuti Basu Roy (NJIT)Alex Quinn (Purdue University) Atsuyuki Morihsima (University of Tsukuba) Program Committee
Contacthmdata.chairs@gmail.com | ||||||
