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@INPROCEEDINGS{Fornaroli_PDC_2026,
                      author = {Fornaroli, Alessandro and Annapureddy, Ravinithesh and Gatica-Perez, Daniel},
                    keywords = {Collaborative Data Analysis, Cross-Sector Collaboration, Data Literacy, Workshop},
                    projects = {Idiap, ICARUS},
         mainresearchprogram = {AI for Everyone},
                       month = jun,
                       title = {DDialogue: A Collaborative Framework for Cross-Sectoral Dialogue through Data},
                   booktitle = {Participatory Design Conference 2026, , June 15--19, 2026, Milan, Italy},
                      volume = {1: Full Papers},
                        year = {2026},
                   publisher = {ACM},
                        isbn = {979-8-4007-2105-2/2026/06},
                         doi = {10.1145/3796624.3796665},
                    abstract = {In tackling complex societal issues, cross-sector collaboration within interdisciplinary teams can leverage the diverse expertise of individuals from various organizations. Concurrently, data-driven methods are increasingly central to effective decision-making. However, there is a notable gap in research at the intersection of cross-boundary collaboration and participatory data analysis. To address this, we propose \textit{DDialogue}, a conceptual framework designed to facilitate collaborative data analysis within cross-sector partnerships, while simultaneously enhancing data literacy skills among stakeholders.
The \textit{DDialogue} framework is instantiated as a workshop composed of nine sessions, each with a specific purpose, leading to the co-creation of outputs aimed at tackling complex issues. 
The framework was tested through a pilot workshop and a fully implemented workshop addressing the problem of juvenile delinquency in Turin, Italy. These activities yielded formative evidence suggesting that \textit{DDialogue} can be a useful tool for researchers or practitioners who seek to engage diverse stakeholders from different sectors and with varying levels of data literacy in data-driven projects. Participant feedback highlighted the framework’s adaptability and its potential to support data-informed collaboration.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2026/Fornaroli_PDC_2026.pdf}
}