Pika: Empowering Non-Programmers to Author Executable Governance Policies in Online Communities
October 06, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Leijie Wang, Nicolas Vincent, Julija RukanskaitΔ, Amy X. Zhang
arXiv ID
2310.04329
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
Internet users have formed a wide array of online communities with nuanced and diverse community goals and norms. However, most online platforms only offer a limited set of governance models in their software infrastructure and leave little room for customization. Consequently, technical proficiency becomes a prerequisite for online communities to build governance policies in code, excluding non-programmers from participation in designing community governance. In this paper, we present Pika, a system that empowers non-programmers to author a wide range of executable governance policies. At its core, Pika incorporates a declarative language that decomposes governance policies into modular components, thereby facilitating expressive policy authoring through a user-friendly, form-based web interface. Our user studies with 17 participants show that Pika can empower non-programmers to author governance policies approximately 2.5 times faster than programmers who author in code. We also provide insights about Pika's expressivity in supporting diverse policies that online communities want.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted