CHI Conference on Human Factors in Computing Systems 2022
DOI: 10.1145/3491102.3517716
|View full text |Cite
|
Sign up to set email alerts
|

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

Abstract: HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and sy… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
12
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 40 publications
(15 citation statements)
references
References 112 publications
0
12
0
Order By: Relevance
“…Prior work noted that stakeholders with little to no background in data science or AI (e.g., domain experts, UX designers, policymakers, etc) might be involved in the design of an AI system's user interface, but rarely in conversations around the objective of the underlying model or the overall problem formulation [39,41,109,133,144]. Recently, a growing body of work in HCI and AI has called for human-centered approaches for broadening participation in AI design to meaningfully engage domain stakeholders to brainstorm and reflect on whether an envisioned future technology is in fact addressing the right problem in the first place [10,34,35,40,70,78,151].…”
Section: Designing Ai With Domain Stakeholdersmentioning
confidence: 99%
“…Prior work noted that stakeholders with little to no background in data science or AI (e.g., domain experts, UX designers, policymakers, etc) might be involved in the design of an AI system's user interface, but rarely in conversations around the objective of the underlying model or the overall problem formulation [39,41,109,133,144]. Recently, a growing body of work in HCI and AI has called for human-centered approaches for broadening participation in AI design to meaningfully engage domain stakeholders to brainstorm and reflect on whether an envisioned future technology is in fact addressing the right problem in the first place [10,34,35,40,70,78,151].…”
Section: Designing Ai With Domain Stakeholdersmentioning
confidence: 99%
“…The mitigation strategies for these harms depend on the use cases and context. Popular strategies include algorithmic and sociotechnical approaches [192], such as improving the training data to mitigate social stereotypes and biases [173]; fine-tuning LLM models on curated datasets [64]; filtering LLM outputs [194,205]; employing special decoding techniques [93,158], adding instructions in prompts [9], monitoring the use of LLMs [192]; as well as inclusive product design and development from the beginning [34,36,75,83]. Building on this prior work, Farsight introduces a novel framework that leverages human-AI collaboration to help AI prototypers identify the potential harms of LLMs.…”
Section: Identifying and Mitigating Llm Harmsmentioning
confidence: 99%
“…This can also require reorienting the traditional HCI paradigm, and providing support for non-experts to actively shape research objectives [95]. When community stakeholders assume a more directive role in research processes, community-driven collaboration can hearken community wisdom and showcase alternative types of knowledge not traditionally surfaced in the design process [20,46,86]. In particular, Lu et al detailed how community events can play a critical role in fostering participatory action research with underserved communities [51].…”
Section: Community-driven Research In Computingmentioning
confidence: 99%
“…Second, we contribute empirical findings of local entrepreneurs' use of generative AI technologies, as well as their concerns for use as it relates to their business. Third, we contribute details of an approach to designing community-driven AI workshops [20] that prioritize long-term commitment (e.g., a four year and ongoing tech support program [44]), community-driven goals (e.g., workshop series embedded in ongoing community initiatives), and community-centered value generation (e.g., workshop series primarily aimed to support entrepreneurs and improve services within the community center).…”
Section: Introductionmentioning
confidence: 99%