*CALL FOR PARTICIPATIONACM AVI 2020 Workshop* *Data4Good: Designing for Diversity and Development* https://sites.google.com/view/data4good/home September 28th-October 2nd Island of Ischia, Italy ***Remote attendance will be facilitated*** OVERVIEW Even as computing makes transformative advancements in data-driven sub-disciplines such as AI and ML, and we witness unprecedented datafication of the society we live in, there is also growing awareness that emergent applications are systematically discriminating against many populations. A major driver of the bias is data, or the lack thereof. As long as the data are drawn using methods that align with a Western, universalist approach, gender, ethnic, racial, and cultural biases are likely to persist. Zou and Schiebinger (2018) have noted that over 45% of Imagenet data, which computer vision research draws from significantly, comes from the United States (US), where only 4% of the world's population resides. India and China together contribute only 3% of Imagenet data, while representing 36% of the world's population. The result is that a photograph of a traditional US bride is annotated accurately, while a photograph of a North Indian bride is recognized as 'performance art' and 'costume'. Given this lack of representation in data, problems related to the robustness and representativeness of data infrastructures become more pressing. For data to be meaningful, they must be collected, stored, understood, analysed, and visualised, all from a holistic and contextually appropriate perspective. There may be challenges encountered in each of these stages; these challenges are exacerbated when we consider the cultural, technological, and/or infrastructural specificities of multilingually diverse and resource-constrained regions across the world. This is true for parts of the Global North as well as the Global South. In many application domains such as global health, education, gender equality, agriculture, and others, the data burden is borne by workers from socio-culturally and economically diverse backgrounds. Low digital expertise and different vantage points can mean that these workers lack the kind of data literacies required of them by their employers. Data-driven approaches can benefit from integration of a more human-centered orientation before being used to inform the design, deployment, and evaluation of technologies in various less-served contexts. These are some of the important conversations that our workshop seeks to advance. CONTRIBUTIONS We invite researchers and practitioners in interdisciplinary domains intersecting HCI, AI, ML, design, STS, and/or global development to engage in dialog on the topics above. A key priority of our workshop will be to invite submissions from an intellectually diverse and global group of participants to further discussions on how appropriate human-centered design can contribute to addressing data-related challenges among marginalised and under-represented/underserved groups around the world. In particular, we solicit participation across more and less technical researchers in HCI who are motivated to address the list of topics below, including but not limited to: *Interfaces and visualization:* - Novel interfaces for deriving qualitative/quantitative insights from data - Interfaces to support data literacy in multilingual contexts - Information visualization tools and techniques for data literacy - Data literacy for end-users in under-resourced contexts *Data infrastructures for social good:* - Data collection and field research - Data quality - Data sharing - Privacy and transparency in data analytics *Human-centered design of data-driven approaches:* - Interfaces for explainable AI - User-centered evaluations, techniques, and methods for AI and social good - Study of public concerns with AI-based technologies *Data work in specific application areas, such as:* - Public/global health - Education - Agriculture - Gender equality - Refugee resettlement - and more. PARTICIPATION We invite submissions of position papers in the ACM CHI Extended Abstracts format, 2-4 pages in length. PDFs of submissions can be emailed to Luigi De Russis at luigi.derussis@polito.it <http://luigi.derussis@polito.it./>. These will be reviewed by all organisers based on relevance, originality, and overall quality. At least one author of each accepted paper is required to participate in our workshop. All workshop participants (including non-authors) are required by the conference to register for the workshop (not necessarily for the conference). We do plan to facilitate remote attendance. OUTCOMES Accepted and presented papers will be made available on CEUR Workshop Proceedings, while workshop results will be published on our website. Notifications will be mailed to the authors within 15 days of receipt (and no later than the dates reported above). Workshop results will be summarized and submitted as an article or blog post in Interactions or Communications of the ACM. DATES *July 28th: Submission deadlineAugust 6th: Notification of acceptanceAugust 21st: Camera-readies and registration dueSeptember 28th: Half-day workshop, from 14:00 to 18:00September 28th-October 2nd: AVI 2020 (main conference)* ORGANISERS Luigi De Russis, Politecnico di Torino, Italy Neha Kumar, Georgia Tech, USA Akhil Mathur, Nokia Bell Labs & University College London, UK