The workshop program included seven workshops, including Auditing Algorithms from the Outside: Methods and Implications; Digital Placemaking: Augmenting Physical Places with Contextual Social Data; Modeling and Mining Temporal Interactions; Religion on Social Media; Standards and Practices in Large-Scale Social Media Research; the ICWSM Science Slam; and Wikipedia, a Social Pedia: Research Challenges and Opportunities. This article contains the written reports of six of the workshops. The Auditing Algorithms from the Outside workshop was organized by Mike Ananny (University of Southern California), Karrie Karahalios (University of Illinois in Urbana-Champaign), Christian Sandvig (University of Michigan), and Christo Wilson (Northeastern University). The organizers did not submit a report to AI Magazine. No technical report was issued. People have embraced social media as a means to express their experiences within and knowledge about particular places, and researchers have continued to analyze these digital traces in order to better understand social activities within particular places. Geotagged social media data such as photos, tweets, check-ins, audio, video, and status updates have proliferated and reveal individual and collective senses of place and local insights into interactions between people and place. However, these digital traces alone cannot reveal a holistic sense of place and placemaking. The workshop aimed to investigate definitions of placemaking -- commonly understood as a collaborative, community, or human-centered approach to the planning, design, social production of public spaces in order to cultivate shared value and recognize specific physical, cultural, and social identities and experiences in a particular place -- and how placemaking intersects with digital and social media.
Garcia, David (ETH Zurich) | Halegoua, Germaine (University of Kansas) | Mejova, Yelena (Qatar Computing Research Institute.) | Perra, Nicola (Northeastern University) | Pfeffer, Jürgen (Carnegie Mellon University) | Ruths, Derek (McGill University) | Weber, Ingmar (Qatar Computing Research Institute) | West, Robert (Stanford University) | Zia, Leila (Wikimedia Foundation)
The 2015 workshops at the International AAAI Conference on Web and Social Media were held on May 26 in Oxford, UK. The workshop program included seven workshops, including Auditing Algorithms From the Outside: Methods and Implications, Digital Placemaking: Augmenting Physical Places with Contextual Social Data, Modeling and Mining Temporal Interactions Religion on Social Media, Standards and Practices in Large-Scale Social Media Research, Wikipedia, a Social Pedia: Research Challenges and Opportunities, and The ICWSM Science Slam. This article contains the written reports of 5 of the workshops
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and discuss our views on reproducibility.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing resources that have been developed for neuroimaging data, and the role of data standards (particularly the Brain Imaging Data Structure) in enabling the automated sharing, processing, and reuse of large neuroimaging datasets. We outline how the open-source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
We propose the creation of a systematic effort to identify and replicate key findings in neuroscience and allied fields related to understanding human values. Our aim is to ensure that research underpinning the value alignment problem of artificial intelligence has been sufficiently validated to play a role in the design of AI systems.