Netherlands

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.

HCOMP 2020

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

wicked problems

social computing

participatory design

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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. 


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.

Using Machine Learning in an Artistic Context: Classification and Consquences

User engagement plays a central role in companies and organisations that aimed to personalise their online services. A main challenge is to leverage knowledge about the online interaction of users to understand what engages them. A common way that engagement is measured and understood is through the definition and development of metrics of user satisfaction, which can act as a proxy of user engagement. This talk will present various works and personal thoughts on how to measure user engagement. It will discuss the definition and development of metrics of user satisfaction that can be used as a proxy of user engagement. An important message is that, for personalisation to work both in the short and the long-term, it is important to consider the heterogeneity of both user and content to formalise the notion of engagement, and in turn design the appropriate metrics to capture these and optimize for. One way to achieve this is to follow these four steps: 1) Understanding intents; 2) Optimizing for the right metric; 3) Acting on segmentation; and 4) Thinking about diversity. The talk will illustrate these steps with some of the research at Spotify.

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