Crowd-powered creativity support systems

Oppenlaender, Jonas

doi:10.1145/3393672.3398646

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Our team consists of scholars and industry leaders working in and across CSCW, HCI, IR and Crowdsourcing. In addition to a strong record of being part of conference organising activities, the team also have prior experience with related workshops [24,29] and running challenges [25,30].…”

Section: Organisersmentioning

confidence: 99%

Investigating and Mitigating Biases in Crowdsourced Data

Hettiachchi

Sanderson

Gonçalves

et al. 2021

Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing

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It is common practice for machine learning systems to rely on crowdsourced label data for training and evaluation. It is also wellknown that biases present in the label data can induce biases in the trained models. Biases may be introduced by the mechanisms used for deciding what data should/could be labelled or by the mechanisms employed to obtain the labels. Various approaches have been proposed to detect and correct biases once the label dataset has been constructed. However, proactively reducing biases during the data labelling phase and ensuring data fairness could be more economical compared to post-processing bias mitigation approaches. In this workshop, we aim to foster discussion on ongoing research around biases in crowdsourced data and to identify future research directions to detect, quantify and mitigate biases before, during and after the labelling process such that both task requesters and crowd workers can benefit. We will explore how specific crowdsourcing workflows, worker attributes, and work practices contribute to biases in the labelled data; how to quantify and mitigate biases as part of the labelling process; and how such mitigation approaches may impact workers and the crowdsourcing ecosystem. The outcome of the workshop will include a collaborative publication of a research agenda to improve or develop novel methods relating to crowdsourcing tools, processes and work practices to address biases in crowdsourced data. We also plan to run a Crowd Bias Challenge prior to the workshop, where participants will be asked to collect labels for a given dataset while minimising potential biases. CCS CONCEPTS• Human-centered computing → Collaborative and social computing.

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Section: Organisersmentioning

confidence: 99%

Investigating and Mitigating Biases in Crowdsourced Data

Hettiachchi

Sanderson

Gonçalves

et al. 2021

Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing

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“…As CS platforms conveniently allow recruiting participants for creativity studies and eliciting creative work, CS platforms have become excellent means for creativity-oriented research and potential sources for supporting creativity in crowd-powered creativity support tools [52]. In our study, we provide an in-depth worker-focused look into the space of using crowdsourcing platforms in creativity-oriented research.…”

Section: Creative Work On Crowdsourcing Platformsmentioning

confidence: 99%

Creativity on Paid Crowdsourcing Platforms

Oppenlaender

Milland

Visuri

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Crowdsourcing platforms are increasingly being harnessed for creative work. The platforms' potential for creative work is clearly identified, but the workers' perspectives on such work have not been extensively documented. In this paper, we uncover what the workers have to say about creative work on paid crowdsourcing platforms. Through a quantitative and qualitative analysis of a questionnaire launched on two different crowdsourcing platforms, our results revealed clear differences between the workers on the platforms in both preferences and prior experience with creative work. We identify common pitfalls with creative work on crowdsourcing platforms, provide recommendations for requesters of creative work, and discuss the meaning of our findings within the broader scope of creativity-oriented research. To the best of our knowledge, we contribute the first extensive worker-oriented study of creative work on paid crowdsourcing platforms.

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Crowd-powered creativity support systems

Cited by 2 publications

References 27 publications

Investigating and Mitigating Biases in Crowdsourced Data

Investigating and Mitigating Biases in Crowdsourced Data

Creativity on Paid Crowdsourcing Platforms

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