A Framework for AI-Supported Mediation in Community-based Online Collaboration
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911154926780416 |
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| author | Cho, Soobin Zachry, Mark McDonald, David W. |
| author_facet | Cho, Soobin Zachry, Mark McDonald, David W. |
| contents | Online spaces involve diverse communities engaging in various forms of collaboration, which naturally give rise to discussions, some of which inevitably escalate into conflict or disputes. To address such situations, AI has primarily been used for moderation. While moderation systems are important because they help maintain order, common moderation strategies of removing or suppressing content and users rarely address the underlying disagreements or the substantive content of disputes. Mediation, by contrast, fosters understanding, reduces emotional tension, and facilitates consensus through guided negotiation. Mediation not only enhances the quality of collaborative decisions but also strengthens relationships among group members. For this reason, we argue for shifting focus toward AI-supported mediation. In this work, we propose an information-focused framework for AI-supported mediation designed for community-based collaboration. Within this framework, we hypothesize that AI must acquire and reason over three key types of information: content, culture, and people. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Framework for AI-Supported Mediation in Community-based Online Collaboration Cho, Soobin Zachry, Mark McDonald, David W. Human-Computer Interaction Online spaces involve diverse communities engaging in various forms of collaboration, which naturally give rise to discussions, some of which inevitably escalate into conflict or disputes. To address such situations, AI has primarily been used for moderation. While moderation systems are important because they help maintain order, common moderation strategies of removing or suppressing content and users rarely address the underlying disagreements or the substantive content of disputes. Mediation, by contrast, fosters understanding, reduces emotional tension, and facilitates consensus through guided negotiation. Mediation not only enhances the quality of collaborative decisions but also strengthens relationships among group members. For this reason, we argue for shifting focus toward AI-supported mediation. In this work, we propose an information-focused framework for AI-supported mediation designed for community-based collaboration. Within this framework, we hypothesize that AI must acquire and reason over three key types of information: content, culture, and people. |
| title | A Framework for AI-Supported Mediation in Community-based Online Collaboration |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.10015 |