More Like this

1. Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity

   https://doi.org/10.1145/3706599.3706746  

   Wu, Shaomei; Wenzel, Kimi; Li, Jingjin; Li, Qisheng; Pradhan, Alisha; Kushalnagar, Raja; Lea, Colin; Koenecke, Allison; Vogler, Christian; Hasegawa-Johnson, Mark; et al (April 2025, ACM)

   Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain. 

   more » « less
2. Building an Infrastructure for Computer Science Education Research and Practice at Scale

   https://doi.org/10.1145/3386527.3405936  

   Brusilovsky, Peter; Koedinger, Ken; Joyner, David A.; Price, Thomas W. (August 2020, Proceedings of the Seventh ACM Conference on Learning at Scale)

   The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues. 

   more » « less
3. Artificial Intelligence for the Electron Ion Collider (AI4EIC)

   https://doi.org/10.1007/s41781-024-00113-4  

   Allaire, C; Ammendola, R; Aschenauer, E-C; Balandat, M; Battaglieri, M; Bernauer, J; Bondì, M; Branson, N; Britton, T; Butter, A; et al (December 2024, Computing and Software for Big Science)

   Abstract The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. 

   more » « less
4. US-Serbia and West Balkan Data Science Workshop

   Obradovic, Zoran (January 2020, NSF Workshop Reports)

   The project mission was to organize a workshop aimed to explore how the US data science community can cooperate with and benefit from collaborations with partners in Serbia and the West Balkan region. The scope included fundamental data science methods and high-impact applications related to big data processing, security and privacy in critical infrastructures, biomedical informatics, and computational archeology. The proposed workshop facilitated closing the gap between data science research in the US and Serbia and the region and brought together data scientists with researchers from disciplines that until recently had little exposure to data science methods, potentially enabling collaborative breakthroughs in those scientific fields. A large fraction of participants from both sides were early career researchers including advanced level graduate students, postdoctoral research associates, and assistant/associate professors within 10 years of obtaining their Ph.D. The participants included a large fraction of female and minority scientists. The workshop objective was achieved by including the following inter-related objectives: (1) Establishing new multidisciplinary international collaborations between data science, mathematics, and sciences that generate big data and require advanced methods; (2) Reinforcing collaboration mechanisms between the NSF and Serbia’s Ministry of Education, Science and Technological Development and organize joint research projects; and (3) Widening the impact of the workshop, by involving researchers and stakeholders from the West Balkan region. The workshop consisted of four tracks, each co-chaired by 3 investigators from the US, Serbia and another West Balkan country. Tangible outcomes from the workshop include a report describing workshop activities for each of four tracks and a proposal recommending research collaboration areas of interest for all parties and determining collaboration mechanisms and programs to facilitate collaboration. 

   more » « less
5. 8th Educational Data Mining in Computer Science Education (CSEDM) Workshop

   https://doi.org/10.5281/zenodo.12730035  

   Shi, Yang; Brusilovsky, Peter; Akram, Bita; Price, Thomas W; Leinonen, Juho; Koedinger, Kenneth R; Lan, Andrew (July 2024, Proceedings of 17th International Conference on Educational Data Mining (EDM 2024), International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   There is a growing community of researchers at the intersection of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a discussion among this research community, with a focus on how data mining can be uniquely applied in computing education research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodologies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day hybrid workshop will feature paper presentations and discussions to promote collaboration. 

   more » « less