Netherlands

We propose a crowdsourced approach for assessing political bias among news publishers. We aggregate pairwise comparisons between publishers into a relative ranking of leanings, in contrast to prior crowdsourcing approaches that request in-dependent ratings of publishers. We verify our approach by a preliminary study with 45 participants, who were asked to rank the political bias of 8 news publishers and to provide qualitative feedback on the study. We report our experimental results and describe our envisioned application scenario.

HCOMP 2020

Assessing Political Bias using Crowdsourced Pairwise Comparisons

political bias

journalism

pairwise comparison

crowdsourcing

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. 


technical paper

How to obtain higher quality task results with a fixed budget (labor cost) is one of the most important problems in practical crowdsourcing. We approach this problem by (1) dynamically choosing appropriate sets of tasks and workers during the worker-task assignment process and (2) introducing an ML worker that performs tasks as if it was a human worker. Our approach, named Dynamic Worker and Task Assignment with ML workers (DWTAML), chooses tasks and workers during the worker-task assignment process, based on the estimation of the reliability of workers and the confidence of task results at each moment. It extends the Dawid&Skene model and IEThresh. The results of an extensive set of experiments showed that DWTAML is effective in most cases.

Dynamic Worker-Task Assignment for High-Quality Task Results with ML Workers

Crowdsourcing offers a new form of work that makes it easy for people to work from home on their own computers. However, for some people with disabilities, such as visual impairment, problems are encountered in completing tasks. In this study, a questionnaire was used to investigate the working conditions of and problems encountered by visually impaired people on Japanese and US crowdsourcing platforms. The problems overcome by visually impaired people, difference between the visually impaired crowd workers from different countries, and possible solutions are provided herein. This research can be extended to more crowdsourcing platforms and languages in the future.

A Survey of Visually Impaired Workers in Japanese and US Crowdsourcing Platforms

In crowdsourcing, although there is a plenty of benefits in terms of cost and time to solve a complex task, there remain many challenges to reach into a consensus judgment from multiple crowd opinions. In this paper, we pay attention to a recently introduced crowd judgment model, termed as ``Constrained Crowd Judgment Analysis'' model. Unlike the traditional crowdsourced opinions (i.e., binary/multiple opinions), the option set in constrained judgment are undefined and there exist multiple components for a particular question. Moreover, there exist some constraints that should be preserved while deriving the final judgment, hence the problem becomes more difficult. Additionally, it is very difficult to rank the crowd workers as there is no ground truth present in this constrained judgment setting. In this paper, we introduce a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based multi-objective model for reaching consensus judgment from multiple crowd opinions as well as providing a better ranking among the crowd worker. The preliminary experimental study over a crowdsourced dataset demonstrates the effectiveness of the proposed model.

A TOPSIS-based Multi-objective Model for Constrained Crowd Judgment Analysis

Folk theories represent the users' beliefs of how an AI-driven system operate. Prior research investigated folk theories as a lens on users' reasoning about the algorithmically-curated content of news feeds on social media platforms primarily in interview studies and surveys. In this work-in-progress, we present an interactive interface for the structured capturing and crowdsourced collection of folk theories at scale.

Socially Augmented Crowdsourced Collection of Folk Theories

Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations (e.g. for Natural Language Processing) suggests using qualifications such as a minimum number of previously completed tasks. If most requesters who pay fairly use this kind of minimum qualification, then workers may be forced to complete a substantial amount of poorly paid work for other requesters before they can earn a fair wage. This paper (1) explores current conventions for the threshold, (2) discusses possible alternatives, and (3) presents a study of correlation between approved work and work quality.

Qualification Labour: A Fair Wage Isn’t Enough if Workers Need to Do 5,000 Low Paid Tasks to Qualify for Your Task

This work demonstrates how semi-supervised learning and human-in-the-loop crowdsourcing can help neural machine translation (NMT) challenges common in low-resource languages. We focus on the Mande language Bambara, which has approximately 16 million primary and secondary speakers in Western Africa. Bambara is mainly spoken as opposed to written language and it has few digital resources due to its history in regions where colonial French became the language of government and industry. Thus, Bambara is a "low-resource language" and because it lacks the existing language resources (parallel digital text and labeled data) necessary for NMT, we describe a novel crowdsourcing approach to support semi-supervised NMT. We designed a crowdsourcing platform that requests the annotator to supply information when the NMT model has decision confusion. Our crowdsourcing platform was tested on evaluating translations of Malian broadcast news and Wikipedia pages in Bambara. Our initial research shows a wide variation in the quality of the translations and further work includes a more rigorous evaluation of translator skills when onboarding new annotators.

Towards a Crowdsourcing Platform for Low Resource Languages -- A Collectivist Approach

Workers on crowdsourcing platforms suffer from low working conditions, including low hourly income, the difficulty of communication with job requesters and work stress due to a lack of social belonging. Research has revealed the great potential that worker cooperation could improve overall efficiency and get them more engaged in crowd work. 
In this presentation, I will introduce my research on how to improve crowd working conditions through worker collaboration with a novel Computer-supported cooperative work (CSCW) system. Following the Design Science Research Methodology (DSRM), each sub-system will be designed to meet a specific function like peer learning and task recommendation. Controlled experiments will be conducted to evaluate the effectiveness of the function and the influence on working conditions.

Improving Crowd Working Conditions with A Novel Cooperation System

We present our work in progress platform Turkish Judge, a crowd-driven adjudication system for rejected work on Amazon Mechanical Turk. The Mechanical Turk crowdsourcing platform allows Requesters to approve or reject assignments submitted by Workers. If the work is rejected, then Workers aren't paid, and their reputation suffers. Currently, there is no built-in mechanism for Workers to appeal rejections, other than contacting Requesters directly. The time it takes Requesters to review potentially incorrectly rejected tasks means that their costs are substantially higher than the payment amount that is in dispute. As a solution to this issue, we present an automated appeals system called Turkish Judge which employs crowd workers as judges to adjudicate whether work was fairly rejected when their peers initiate an appeal.

Turkish Judge: A Peer Evaluation Framework for Mechanical Turk Appeals

For many applications, human cognitive limitations are something that poses challenging constraints. In this paper, I argue that they also have their advantages: they enable new systems and functionality that would be impossible without them. This creates an opportunity for new and exciting interdisciplinary collaborations between cognitive science and computer science.

Using Human Cognitive Limitations to Enable New Systems

Crowd-powered innovation platforms act to a large degree as silos: they cater the cognitive surplus of a bespoke, self-selected audience to a limited amount of high-value clients in a model where both stakeholders typically have to jump through a series of hoops to enrol to the services. We propose a fundamentally disruptive way for discovery with distributed crowds, by orchestrating already established online audiences for serendipitous crowdsourcing. Two recent developments make our proposition, The Planetary Cognition Delivery Network, compelling right now. First, online properties are struggling due to declining advertising revenues, caused by the proliferation of ad-blockers and a few key Internet giants taking an increasingly larger cut of the available revenue. Second, and perhaps somewhat counter-intuitively to the immediate perception in the Western world, the Internet is just now becoming widely available in many corners of the world, which provides an opportunity for a truly worldwide reach during the next decade or two. COGNET is designed to offer a frictionless participation mechanism for all three key stakeholders: requesters, providers, and contributors. It essentially orchestrates a distributed network of human cognition pools for arbitrary discovery tasks that could benefit from the vast cognitive surplus available through the internet.


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Dynamic Worker-Task Assignment for High-Quality Task Results with ML Workers

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A TOPSIS-based Multi-objective Model for Constrained Crowd Judgment Analysis

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Next from HCOMP 2020

Dynamic Worker-Task Assignment for High-Quality Task Results with ML Workers

Similar lecture

A TOPSIS-based Multi-objective Model for Constrained Crowd Judgment Analysis

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