Netherlands

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&#39;&#39; 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.

HCOMP 2020

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

topsis

multi-objective optimization

crowdsourcing

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

technical paper

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. 


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.


COGNET: The Planetary Cognition Delivery Network

The field of crowdsourcing and human computation has seen a steady invention of technology and new applications of technology to solve increasingly complex problems. We propose a new framework that serves as a complement to incremental innovation by assuming that some seemingly intractable social-scale problems can be solved, suggesting a crowd-ready process to subdivide the problem by parts and by phase and then seeking development of the needed technology. The framework outlines a technology-assisted human computation process which results in the participatory design of what we call a social problem-solving model—an actionable community story that enjoys the buy-in from the community because it is developed by the community. We propose reframing a given social problem in terms of a community-determined superordinate goal and then recursively subdividing obstacles to that goal into larger numbers of simpler obstacle subcomponents, which better lend themselves to community-proposed solutions. The question of resource matching to implement these solutions is handled as a separate step. Technology tools need to be developed to facilitate each phase of this process. We discuss potential beneficial characteristics of the problem-solving model designed by the community and the nature of the technology required to implement each phase of the process. Finally, we discuss limitations we foresee to this approach.

A Technology-Assisted Social Computing Framework for Solving Complex Social Problems

We live in the Information Age where information is vital to our work. This implies that the more information we have, the better equipped we are to perform work. Working under the constant drive to be more productive, it is a foreseeable future where everyone works in synergy and partnership with AI agents in order to do more. To achieve this, a preferably simple and robust framework should facilitate the collaboration across a network of different humans and machines. As advancements in AI (narrow or otherwise) models continue, we will invariably reach a stage where humans and AI need to coexist in an interactive and personalised manner different from today’s largely invisible AI mainly operating autonomously in the background. In this work, our main contribution is the creation of a type theoretic framework enabling the orchestration of human-expressed intent(s), associated data and implementation(s) in a distributed manner which mirrors real world complex working environments where work plans are evolving and even disjoint due to lack of bigger picture.

Group-Assign: Type Theoretic Framework for Human AI Orchestration

As a visual artist, datasets are a core part of both my process and output. I will talk about what I believe at dataset to be; the compiling and training machine learning on datasets; exploring how datasets are created and possibilities for overcoming bias the different ways they can be constructed and some of the the issues, problems, solutions with working with them. In particular I will be focusing on classification and the consequences of using it in datasets, machine learning, and artistic works.

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Socially Augmented Crowdsourced Collection of Folk Theories

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